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Xiaofeng Zhu

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36 papers

2 author rows

AAAI Conference 2026 Conference Paper

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees

Zhiyuan Wang
Jinhao Duan
Qingni Wang
Xiaofeng Zhu
Tianlong Chen
Xiaoshuang Shi
Kaidi Xu

Uncertainty quantification (UQ) in foundation models is crucial for identifying and mitigating hallucinations in automatically generated text. However, heuristic UQ approaches lack statistical guarantees for key metrics such as the false discovery rate (FDR) in selective prediction tasks. Previous research adopts the split conformal prediction (SCP) framework to ensure desired coverage of admissible answers by constructing data-driven prediction sets, yet these sets typically contain incorrect candidates, undermining their practical effectiveness. To address this, we introduce COIN, an uncertainty-guarding selection framework that calibrates statistically valid uncertainty thresholds to filter a single generated answer per question under user-specified FDR constraints. COIN estimates the empirical error rate on the calibration set and applies confidence interval methods such as Clopper–Pearson to establish a high-probability upper bound on the true error rate (i.e., FDR). This enables the selection of the largest threshold that ensures FDR control on test data while significantly increasing sample retention. We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data across both general and multimodal text generation tasks. Furthermore, we show that employing alternative UQ and upper bound construction strategies can further boost COIN's power performance, which underscores its extensibility and adaptability to diverse application scenarios.

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AAAI Conference 2026 Conference Paper

Cross-modal Prompting for Balanced Incomplete Multi-modal Emotion Recognition

Wen-Jue He
Xiaofeng Zhu
Zheng Zhang

Incomplete multi-modal emotion recognition (IMER) aims at understanding human intentions and sentiments by comprehensively exploring the partially-observed multi-source data. Although the multi-modal data is expected to provide more abundant information, the performance gap and modality under-optimization problem hinder effective multi-modal learning in practice, and are exacerbated in the confrontation of the missing data. To address this issue, we devise a novel Cross-modal Prompting (ComP) method, which emphasizes coherent information by enhancing modality-specific features and improves the overall recognition accuracy by boosting each modality's performance. Specifically, a progressive prompt generation module with a dynamic gradient modulator is proposed to produce concise and consistent modality semantic cues. Meanwhile, cross-modal knowledge propagation selectively amplifies the consistent information in modality features with the delivered prompts to enhance the discrimination of the modality-specific output. Additionally, a coordinator is employed to dynamically re-weight the modality outputs as a complement to the balance strategy to improve the model's efficacy. Extensive experiments on 4 datasets with 7 SOTA methods under different missing rates validate the effectiveness of our proposed method.

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AAAI Conference 2026 Conference Paper

Graph Smoothing for Enhanced Local Geometry Learning in Point Cloud Analysis

Shangbo Yuan
Jie Xu
Ping Hu
Xiaofeng Zhu
Na Zhao

Graph-based methods have proven to be effective in capturing relationships among points for 3D point cloud analysis. However, these methods often suffer from suboptimal graph structures, particularly due to sparse connections at boundary points and noisy connections in junction areas. To address these challenges, we propose a novel method that integrates a graph smoothing module with an enhanced local geometry learning module. Specifically, we identify the limitations of conventional graph structures, particularly in handling boundary points and junction areas. In response, we introduce a graph smoothing module designed to optimize the graph structure and minimize the negative impact of unreliable sparse and noisy connections. Based on the optimized graph structure, we improve the feature extract function with local geometry information. These include shape features derived from adaptive geometric descriptors based on eigenvectors and distribution features obtained through cylindrical coordinate transformation. Experimental results on real-world datasets validate the effectiveness of our method in various point cloud learning tasks, i.e., classification, part segmentation, and semantic segmentation.

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AAAI Conference 2026 Conference Paper

Meta-GAIN for Missing Data Imputation

Tao Tong
Xiaofeng Zhu
Jiangzhang Gan

Although previous deep imputation methods (eg., Generative Adversarial Network (GAN) based methods) have been widely designed to impute missing data, they still suffer from the issues, ie., lack of the imputation diversity and the generalization ability. In this paper, we propose a new GAN-based imputation method, namely Meta-based Generative Adversarial Imputation Network (Meta-GAIN), to investigate a new generator for achieving diverse imputation and generalization ability. Specifically, we employ the Kullback-Leibler (KL) divergence to achieve the imputation diversity by generating a continuous embedding space of the original data. We also design a task regularizer to suppress redundant features and capture a more authentic distribution, thus enhancing the generalization ability of the imputation model. Moreover, we theoretically prove that our proposed regularizer achieves the generalization ability. In addition, we design a new meta network to efficient optimize our objective function as well as to improve imputation diversity. Experimental results on real datasets show that our method outperforms all comparison methods under different missing mechanisms in terms of imputation and classification performance.

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IJCAI Conference 2025 Conference Paper

Graph Embedded Contrastive Learning for Multi-View Clustering

Hongqing He
Jie Xu
Guoqiu Wen
Yazhou Ren
Na Zhao
Xiaofeng Zhu

Recently, numerous multi-view clustering (MVC) and multi-view graph clustering (MVGC) methods have been proposed. Despite significant progress, they still face two issues: I) MVC and MVGC are often developed independently for multi-view and multi-graph data. They have redundancy but lack a unified methodology to combine their strengths. II) Contrastive learning is usually adopted to explore the associations across multiple views. However, traditional contrastive losses ignore the neighbor relationship in multi-view scenarios and easily lead to false associations in sample pairs. To address these issues, we propose Graph Embedded Contrastive Learning for Multi-View Clustering. Concretely, we propose a process of view-specific pre-training with adaptive graph convolution to make our method compatible with both multi-view and multi-graph data, which aggregates the graph information into data and leverages autoencoders to learn view-specific representations. Furthermore, to explore the view-cross associations, we introduce the process of view-cross contrastive learning and clustering, where we propose the graph-guided contrastive learning that can generate global graph to mitigate the false association issue as well as the cluster-guided contrastive clustering for improving the model robustness. Finally, extensive experiments demonstrate that our method achieves superior performance on both MVC and MVGC tasks.

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IJCAI Conference 2025 Conference Paper

MCD-CLIP: Multi-view Chest Disease Diagnosis with Disentangled CLIP

Songyue Cai
Yujie Mo
Liang Peng
Yucheng Xie
Tao Tong
Xiaofeng Zhu

Pre-trained methods for multi-view chest X-ray images have demonstrated impressive performance in chest disease diagnosis, but there are still some limitations that need to be addressed. Firstly, many pre-trained methods require full fine-tuning pre-trained models to induce significant computational resource usage and the prior knowledge destruction. Secondly, many pre-trained methods cannot efficiently balance consistency and complementarity among views, leading to information loss and performance degradation. To tackle these issues, we propose MCD-CLIP, a CLIP-based multi-view chest disease diagnosis method. It uses visual prompts and a Prompt-Aligner to align prompts across views, along with the additional text representation for efficient transfer. Moreover, we employ Adapters to disentangle the image representation, maintaining consistency and complementarity from different views. Experimental results on the chest X-ray dataset demonstrate that MCD-CLIP achieves comparable or better performance on a variety of tasks with 94. 31% fewer tunable parameters compared to state-of-the-art methods. The source codes are released at https: //github. com/YuzunoKawori/MCD-CLIP.

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IJCAI Conference 2025 Conference Paper

Meta Label Correction with Generalization Regularizer

Tao Tong
Yujie Mo
Yucheng Xie
Songyue Cai
Xiaoshuang Shi
Xiaofeng Zhu

Deep neural networks can easily lead to the over-fitting issue due to the influence of noisy labels. However, previous label correction methods for dealing with noisy labels often need expensive computation cost to achieve effectiveness and ignore the generalization ability of the model. To address these issues, in this paper, we propose a new meta-based self-correction method to achieve accurate filtering of noisy labels and to enhance the generalization ability of the label correction model. Specifically, we first investigate a new gradient score method to filter noisy labels with less computation cost, and then theoretically design a new generalization regularizer into the meta-learner and the base learner, for correcting noisy labels as well as achieving the generalization ability. Experimental results on real datasets verify the effectiveness of our proposed method in terms of different classification tasks.

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AAAI Conference 2025 Conference Paper

Multiplex Graph Representation Learning with Homophily and Consistency

Yudi Huang
Ci Nie
Hongqing He
Yujie Mo
Yonghua Zhu
Guoqiu Wen
Xiaofeng Zhu

Although unsupervised multiplex graph representation learning (UMGRL) has been a hot research topic, existing UMGRL methods still has limitations to be addressed. For example, previous works either preserve structural information by ignoring the impact of heterophily in the graph structure or only focus on node-level consistency by ignoring class-level consistency. To address these issues, in this paper, we propose a new UMGRL method to explore both homophily and consistency in the multiplex graph. Specifically, we propose to restructure the multi-order relationships of every graph between every node and its multi-order neighbors to improve the homophily and reduce the impact of the heterophily in the graph structure. We also design a contrastive loss based on a self-expression matrix of the node representation to achieve node-level and class-level consistency. Furthermore, we theoretically prove our method to achieve class-level consistency. Extensive experimental results on real datasets verify the effectiveness of the proposed method with respect to node classification tasks, compared to SOTA methods.

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ICML Conference 2025 Conference Paper

Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning

Fangwen Wu
Lechao Cheng
Shengeng Tang
Xiaofeng Zhu
Chaowei Fang
Dingwen Zhang
Meng Wang 0001

Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones. Balancing flexibility and stability remains a significant challenge, particularly when the task ID is unknown. To address this, our study reveals that the gap in feature distribution between novel and existing tasks is primarily driven by differences in mean and covariance moments. Building on this insight, we propose a novel semantic drift calibration method that incorporates mean shift compensation and covariance calibration. Specifically, we calculate each class’s mean by averaging its sample embeddings and estimate task shifts using weighted embedding changes based on their proximity to the previous mean, effectively capturing mean shifts for all learned classes with each new task. We also apply Mahalanobis distance constraint for covariance calibration, aligning class-specific embedding covariances between old and current networks to mitigate the covariance shift. Additionally, we integrate a feature-level self-distillation approach to enhance generalization. Comprehensive experiments on commonly used datasets demonstrate the effectiveness of our approach. The source code is available at https: //github. com/fwu11/MACIL. git.

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AAAI Conference 2025 Conference Paper

Noisy Node Classification by Bi-level Optimization Based Multi-Teacher Distillation

Yujing Liu
Zongqian Wu
Zhengyu Lu
Ci Nie
Guoqiu Wen
Yonghua Zhu
Xiaofeng Zhu

Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data. Specifically, we first employ multiple self-supervised learning methods to train diverse teacher models, and then aggregate their predictions through a teacher weight matrix. Furthermore, we design a new bi-level optimization strategy to dynamically adjust the teacher weight matrix based on the training progress of the student model. Finally, we design a label improvement module to improve the label quality. Extensive experimental results on real datasets show that our method achieves the best results compared to state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

Seeking Proxy Point via Stable Feature Space for Noisy Correspondence Learning

Yucheng Xie
Songyue Cai
Tao Tong
Ping Hu
Xiaofeng Zhu

To meet the growing demand for cross-modal training data, directly collecting multimodal data from the Internet has become prevalent. However, such data inevitably suffer from Noisy Correspondence. Previous works focused on recasting soft labels to mitigate noise's negative impact. We explore a novel perspective to solve this problem: pursuing proxy representation for noisy data to enable reliable feature learning. To this end, we propose a novel framework: Seeking Proxy Point via Stable Feature Space (SPS). This framework employs a fine-grained partitioning strategy to obtain a high-confidence reliable set. By imposing intermodal cross-transformation consistency constraints and intramodal metric consistency constraints, a stable feature space is constructed. Building on this foundation, SPS seeks proxy points for noisy data, enabling even noisy data to be accurately embedded into appropriate positions within the feature space. Combined with partial alignment for partially matched data pairs, SPS ultimately achieves robust learning under Noisy Correspondence. Experiments on three widely used cross-modal datasets demonstrate that SPS significantly outperforms previous methods. Our code is available at https: //github. com/C-TeaRanger/SPS.

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IJCAI Conference 2024 Conference Paper

Exploring the Role of Node Diversity in Directed Graph Representation Learning

Jincheng Huang
Yujie Mo
Ping Hu
Xiaoshuang Shi
Shangbo Yuan
Zeyu Zhang
Xiaofeng Zhu

Many methods of Directed Graph Neural Networks (DGNNs) are designed to equally treat nodes in the same neighbor set (i. e. , out-neighbor set and in-neighbor set) for every node, without considering the node diversity in directed graphs, so they are often unavailable to adaptively acquire suitable information from neighbors of different directions. To alleviate this issue, in this paper, we investigate a new way to first consider node diversity for representation learning on directed graphs, i. e. , neighbor diversity and degree diversity, and then propose a new NDDGNN framework to adaptively assign weights to both outgoing information and incoming information at the node level. Extensive experiments on seven real-world datasets validate the superior performance of our method compared to state-of-the-art methods in terms of both node classification and link prediction tasks.

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IJCAI Conference 2024 Conference Paper

Multiplex Graph Representation Learning via Bi-level Optimization

Yudi Huang
Yujie Mo
Yujing Liu
Ci Nie
Guoqiu Wen
Xiaofeng Zhu

Many multiplex graph representation learning (MGRL) methods have been demonstrated to 1) ignore the globally positive and negative relationships among node features; and 2) usually utilize the node classification task to train both graph structure learning and representation learning parameters, and thus resulting in the problem of edge starvation. To address these issues, in this paper, we propose a new MGRL method based on the bi-level optimization. Specifically, in the inner level, we optimize the self-expression matrix to capture the globally positive and negative relationships among nodes, as well as complement them with the local relationships in graph structures. In the outer level, we optimize the parameters of the graph convolutional layer to obtain discriminative node representations. As a result, the graph structure optimization does not depend on the node classification task, which solves the edge starvation problem. Extensive experiments show that our model achieves the superior performance on node classification tasks on all datasets.

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NeurIPS Conference 2024 Conference Paper

Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective

Yujie Mo
Zhihe Lu
Runpeng Yu
Xiaofeng Zhu
Xinchao Wang

Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clustering approaches, they encounter two significant limitations: (i) noise in graph structures is often introduced during the message-passing process to weaken node representations, and (ii) cluster-level information may be inadequately captured and leveraged, diminishing the performance in downstream tasks. In this paper, we address these limitations by theoretically revisiting SHGL from the spectral clustering perspective and introducing a novel framework enhanced by rank and dual consistency constraints. Specifically, our framework incorporates a rank-constrained spectral clustering method that refines the affinity matrix to exclude noise effectively. Additionally, we integrate node-level and cluster-level consistency constraints that concurrently capture invariant and clustering information to facilitate learning in downstream tasks. We theoretically demonstrate that the learned representations are divided into distinct partitions based on the number of classes and exhibit enhanced generalization ability across tasks. Experimental results affirm the superiority of our method, showcasing remarkable improvements in several downstream tasks compared to existing methods.

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AAAI Conference 2024 Conference Paper

Self-Training Based Few-Shot Node Classification by Knowledge Distillation

Zongqian Wu
Yujie Mo
Peng Zhou
Shangbo Yuan
Xiaofeng Zhu

Self-training based few-shot node classification (FSNC) methods have shown excellent performance in real applications, but they cannot make the full use of the information in the base set and are easily affected by the quality of pseudo-labels. To address these issues, this paper proposes a new self-training FSNC method by involving the representation distillation and the pseudo-label distillation. Specifically, the representation distillation includes two knowledge distillation methods (i.e., the local representation distillation and the global representation distillation) to transfer the information in the base set to the novel set. The pseudo-label distillation is designed to conduct knowledge distillation on the pseudo-labels to improve their quality. Experimental results showed that our method achieves supreme performance, compared with state-of-the-art methods. Our code and a comprehensive theoretical version are available at https://github.com/zongqianwu/KD-FSNC.

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IJCAI Conference 2024 Conference Paper

Simple Contrastive Multi-View Clustering with Data-Level Fusion

Caixuan Luo
Jie Xu
Yazhou Ren
Junbo Ma
Xiaofeng Zhu

Previous deep multi-view clustering methods usually design un-shared encoders to explore the cluster information among multi-view data, but they are difficult to customize the encoders for individual views and easily increase information loss. To address these issues, we propose a simple yet effective contrastive multi-view clustering framework. Specifically, different from using feature-level fusion in previous methods, we first propose a data-level fusion method to fuse multi-view information, which produces a fused data to replace all views and thus avoids customizing networks for different views. Then, we simulate the data noise and unavailability in multiple views to design two kinds of data augmentation for the fused data, making a shared encoder with simple contrastive learning to learn robust features and achieve the interaction across views. As a result, our method is a general framework and we base on it to conduct feature clustering and end-to-end clustering. Extensive experiments demonstrate that our method can explore the discriminative information in multi-view data and achieve superior clustering performance.

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IJCAI Conference 2024 Conference Paper

Towards Dynamic-Prompting Collaboration for Source-Free Domain Adaptation

Mengmeng Zhan
Zongqian Wu
Rongyao Hu
Ping Hu
Heng Tao Shen
Xiaofeng Zhu

In domain adaptation, challenges such as data privacy constraints can impede access to source data, catalyzing the development of source-free domain adaptation (SFDA) methods. However, current approaches heavily rely on models trained on source data, posing the risk of overfitting and suboptimal generalization. This paper introduces a dynamic prompt learning paradigm that harnesses the power of large-scale vision-language models to enhance the semantic transfer of source models. Specifically, our approach fosters robust and adaptive collaboration between the source-trained model and the vision-language model, facilitating the reliable extraction of domain-specific information from unlabeled target data, while consolidating domain-invariant knowledge. Without the need for accessing source data, our method amalgamates the strengths inherent in both traditional SFDA approaches and vision-language models, formulating a collaborative framework for addressing SFDA challenges. Extensive experiments conducted on three benchmark datasets showcase the superiority of our framework over previous SOTA methods.

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AAAI Conference 2023 Conference Paper

Dual Label-Guided Graph Refinement for Multi-View Graph Clustering

Yawen Ling
Jianpeng Chen
Yazhou Ren
Xiaorong Pu
Jie Xu
Xiaofeng Zhu
Lifang He

With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can discover the hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to the given graphs, especially influenced by the low quality graphs, i.e., they tend to be limited by the homophily assumption. However, the widespread real-world data hardly satisfy the homophily assumption. This gap limits the performance of existing MVGC methods on low homophilous graphs. To mitigate this limitation, our motivation is to extract high-level view-common information which is used to refine each view's graph, and reduce the influence of non-homophilous edges. To this end, we propose dual label-guided graph refinement for multi-view graph clustering (DuaLGR), to alleviate the vulnerability in facing low homophilous graphs. Specifically, DuaLGR consists of two modules named dual label-guided graph refinement module and graph encoder module. The first module is designed to extract the soft label from node features and graphs, and then learn a refinement matrix. In cooperation with the pseudo label from the second module, these graphs are refined and aggregated adaptively with different orders. Subsequently, a consensus graph can be generated in the guidance of the pseudo label. Finally, the graph encoder module encodes the consensus graph along with node features to produce the high-level pseudo label for iteratively clustering. The experimental results show the superior performance on coping with low homophilous graph data. The source code for DuaLGR is available at https://github.com/YwL-zhufeng/DuaLGR.

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AAAI Conference 2023 Conference Paper

Multiplex Graph Representation Learning via Common and Private Information Mining

Yujie Mo
Zongqian Wu
Yuhuan Chen
Xiaoshuang Shi
Heng Tao Shen
Xiaofeng Zhu

Self-supervised multiplex graph representation learning (SMGRL) has attracted increasing interest, but previous SMGRL methods still suffer from the following issues: (i) they focus on the common information only (but ignore the private information in graph structures) to lose some essential characteristics related to downstream tasks, and (ii) they ignore the redundant information in node representations of each graph. To solve these issues, this paper proposes a new SMGRL method by jointly mining the common information and the private information in the multiplex graph while minimizing the redundant information within node representations. Specifically, the proposed method investigates the decorrelation losses to extract the common information and minimize the redundant information, while investigating the reconstruction losses to maintain the private information. Comprehensive experimental results verify the superiority of the proposed method, on four public benchmark datasets.

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NeurIPS Conference 2023 Conference Paper

Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration

Jie Xu
Shuo Chen
Yazhou Ren
Xiaoshuang Shi
Hengtao Shen
Gang Niu
Xiaofeng Zhu

Recently, numerous studies have demonstrated the effectiveness of contrastive learning (CL), which learns feature representations by pulling in positive samples while pushing away negative samples. Many successes of CL lie in that there exists semantic consistency between data augmentations of the same instance. In multi-view scenarios, however, CL might cause representation degeneration when the collected multiple views inherently have inconsistent semantic information or their representations subsequently do not capture sufficient discriminative information. To address this issue, we propose a novel framework called SEM: SElf-weighted Multi-view contrastive learning with reconstruction regularization. Specifically, SEM is a general framework where we propose to first measure the discrepancy between pairwise representations and then minimize the corresponding self-weighted contrastive loss, and thus making SEM adaptively strengthen the useful pairwise views and also weaken the unreliable pairwise views. Meanwhile, we impose a self-supervised reconstruction term to regularize the hidden features of encoders, to assist CL in accessing sufficient discriminative information of data. Experiments on public multi-view datasets verified that SEM can mitigate representation degeneration in existing CL methods and help them achieve significant performance improvements. Ablation studies also demonstrated the effectiveness of SEM with different options of weighting strategies and reconstruction terms.

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IJCAI Conference 2023 Conference Paper

Totally Dynamic Hypergraph Neural Networks

Peng Zhou
Zongqian Wu
Xiangxiang Zeng
Guoqiu Wen
Junbo Ma
Xiaofeng Zhu

Recent dynamic hypergraph neural networks (DHGNNs) are designed to adaptively optimize the hypergraph structure to avoid the dependence on the initial hypergraph structure, thus capturing more hidden information for representation learning. However, most existing DHGNNs cannot adjust the hyperedge number and thus fail to fully explore the underlying hypergraph structure. This paper proposes a new method, namely, totally hypergraph neural network (TDHNN), to adjust the hyperedge number for optimizing the hypergraph structure. Specifically, the proposed method first captures hyperedge feature distribution to obtain dynamical hyperedge features rather than fixed ones, by conducting the sampling from the learned distribution. The hypergraph is then constructed based on the attention coefficients of both sampled hyperedges and nodes. The node features are dynamically updated by designing a simple hypergraph convolution algorithm. Experimental results on real datasets demonstrate the effectiveness of the proposed method, compared to SOTA methods. The source code can be accessed via https: //github. com/HHW-zhou/TDHNN.

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AAAI Conference 2022 Conference Paper

Deep Incomplete Multi-View Clustering via Mining Cluster Complementarity

Jie Xu
Chao Li
Yazhou Ren
Liang Peng
Yujie Mo
Xiaoshuang Shi
Xiaofeng Zhu

Incomplete multi-view clustering (IMVC) is an important unsupervised approach to group the multi-view data containing missing data in some views. Previous IMVC methods suffer from the following issues: (1) the inaccurate imputation or padding for missing data negatively affects the clustering performance, (2) the quality of features after fusion might be interfered by the low-quality views, especially the inaccurate imputed views. To avoid these issues, this work presents an imputation-free and fusion-free deep IMVC framework. First, the proposed method builds a deep embedding feature learning and clustering model for each view individually. Our method then nonlinearly maps the embedding features of complete data into a high-dimensional space to discover linear separability. Concretely, this paper provides an implementation of the high-dimensional mapping as well as shows the mechanism to mine the multi-view cluster complementarity. This complementary information is then transformed to the supervised information with high confidence, aiming to achieve the multi-view clustering consistency for the complete data and incomplete data. Furthermore, we design an EM-like optimization strategy to alternately promote feature learning and clustering. Extensive experiments on real-world multi-view datasets demonstrate that our method achieves superior clustering performance over state-of-the-art methods.

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IJCAI Conference 2022 Conference Paper

Information Augmentation for Few-shot Node Classification

Zongqian Wu
Peng Zhou
Guoqiu Wen
Yingying Wan
Junbo Ma
Debo Cheng
Xiaofeng Zhu

Although meta-learning and metric learning have been widely applied for few-shot node classification (FSNC), some limitations still need to be addressed, such as expensive time costs for the meta-train and difficult of exploring the complex structure inherent the graph data. To address in issues, this paper proposes a new data augmentation method to conduct FSNC on the graph data including parameter initialization and parameter fine-tuning. Specifically, parameter initialization only conducts a multi-classification task on the base classes, resulting in good generalization ability and less time cost. Parameter fine-tuning designs two data augmentation methods (i. e. , support augmentation and shot augmentation) on the novel classes to generate sufficient node features so that any traditional supervised classifiers can be used to classify the query set. As a result, the proposed method is the first work of data augmentation for FSNC. Experiment results show the effectiveness and the efficiency of our proposed method, compared to state-of-the-art methods, in terms of different classification tasks.

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IJCAI Conference 2022 Conference Paper

Multi-view Unsupervised Graph Representation Learning

Jiangzhang Gan
Rongyao Hu
Mengmeng Zhan
Yujie Mo
Yingying Wan
Xiaofeng Zhu

Both data augmentation and contrastive loss are the key components of contrastive learning. In this paper, we design a new multi-view unsupervised graph representation learning method including adaptive data augmentation and multi-view contrastive learning, to address some issues of contrastive learning ignoring the information from feature space. Specifically, the adaptive data augmentation first builds a feature graph from the feature space, and then designs a deep graph learning model on the original representation and the topology graph to update the feature graph and the new representation. As a result, the adaptive data augmentation outputs multi-view information, which is fed into two GCNs to generate multi-view embedding features. Two kinds of contrastive losses are further designed on multi-view embedding features to explore the complementary information among the topology and feature graphs. Additionally, adaptive data augmentation and contrastive learning are embedded in a unified framework to form an end-to-end model. Experimental results verify the effectiveness of our proposed method, compared to state-of-the-art methods.

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AAAI Conference 2022 Conference Paper

Simple Unsupervised Graph Representation Learning

Yujie Mo
Liang Peng
Jie Xu
Xiaoshuang Shi
Xiaofeng Zhu

In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the interclass variation, as well as adds an upper bound loss to achieve the finite distance between positive embeddings and anchor embeddings for reducing the intra-class variation. As a result, both enlarging inter-class variation and reducing intra-class variation result in a small generalization error, thereby obtaining an effective model. Furthermore, our method removes widely used data augmentation and discriminator from previous graph contrastive learning methods, meanwhile available to output low-dimensional embeddings, leading to an efficient model. Experimental results on various real-world datasets demonstrate the effectiveness and efficiency of our method, compared to state-of-the-art methods. The source codes are released at https: //github. com/YujieMo/SUGRL.

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AAAI Conference 2021 Conference Paper

Multi-scale Graph Fusion for Co-saliency Detection

Rongyao Hu
Zhenyun Deng
Xiaofeng Zhu

The key challenge of co-saliency detection is to extract discriminative features to distinguish the common salient foregrounds from backgrounds in a group of relevant images. In this paper, we propose a new co-saliency detection framework which includes two strategies to improve the discriminative ability of the features. Specifically, on one hand, we segment each image to semantic superpixel clusters as well as generate different scales/sizes of images for each input image by the VGG-16 model. Different scales capture different patterns of the images. As a result, multi-scale images can capture various patterns among all images by many kinds of perspectives. Second, we propose a new method of Graph Convolutional Network (GCN) to fine-tune the multi-scale features, aiming at capturing the common information among the features from all scales and the private or complementary information for the feature of each scale. Moreover, the proposed GCN method jointly conducts multi-scale feature fine-tune, graph learning, and feature learning in a unified framework. We evaluated our method on three benchmark data sets, compared to state-of-the-art co-saliency detection methods. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics.

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IJCAI Conference 2020 Conference Paper

Multi-graph Fusion for Functional Neuroimaging Biomarker Detection

Jiangzhang Gan
Xiaofeng Zhu
Rongyao Hu
Yonghua Zhu
Junbo Ma
Ziwen Peng
Guorong Wu

Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance. In this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i. e. , brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i. e. , fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods.

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IJCAI Conference 2019 Conference Paper

Prediction of Mild Cognitive Impairment Conversion Using Auxiliary Information

Xiaofeng Zhu

In this paper, we propose a new feature selection method to exploit the issue of High Dimension Low Sample Size (HDLSS) for the prediction of Mild Cognitive Impairment (MCI) conversion. Specially, by regarding the Magnetic Resonance Imaging (MRI) information of MCI subjects as the target data, this paper proposes to integrate auxiliary information with the target data in a unified feature selection framework for distinguishing progressive MCI (pMCI) subjects from stable MCI (sMCI) subjects, i. e. , the MCI conversion classification for short in this paper, based on their MRI information. The auxiliary information includes the Positron Emission Tomography (PET) information of the target data, the MRI information of Alzheimer’s Disease (AD) subjects and Normal Control (NC) subjects, and the ages of the target data and the AD and NC subjects. As a result, the proposed method jointly selects features from the auxiliary data and the target data by taking into account the influence of outliers and aging of these two kinds of data. Experimental results on the public data of Alzheimer’s Disease Neuroimaging Initiative (ADNI) verified the effectiveness of our proposed method, compared to three state-of-the-art feature selection methods, in terms of four classification evaluation metrics.

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AAAI Conference 2018 Conference Paper

Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity

Xiaofeng Zhu
Hongming Li
Yong Fan

In contrast to most existing studies that typically characterize the developmental sex differences using analysis of variance or equivalently multiple linear regression, we present a parameter-free centralized multi-task learning method to identify sex speciﬁc and common resting state functional connectivity (RSFC) patterns underlying the brain development based on resting state functional MRI (rs-fMRI) data. Speciﬁcally, we design a novel multi-task learning model to characterize sex speciﬁc and common RSFC patterns in an age prediction framework by regarding the age prediction for males and females as separate tasks. Moreover, the importance of each task and the balance of these two patterns, respectively, are automatically learned in order to make the multi-task learning robust as well as free of tunable parameters, i. e. , parameter-free for short. Our experimental results on synthetic datasets veriﬁed the effectiveness of our method with respect to prediction performance, and experimental results on rs-fMRI scans of 1041 subjects (651 males) of the Philadelphia Neurodevelopmental Cohort (PNC) showed that our method could improve the age prediction on average by 5. 82% with statistical signiﬁcance than the best alternative methods under comparison, in addition to characterizing the developmental sex differences in RSFC patterns.

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IJCAI Conference 2018 Conference Paper

Robust Feature Selection on Incomplete Data

Wei Zheng
Xiaofeng Zhu
Yonghua Zhu
Shichao Zhang

Feature selection is an indispensable preprocessing procedure for high-dimensional data analysis, but previous feature selection methods usually ignore sample diversity (i. e. , every sample has individual contribution for the model construction) andhave limited ability to deal with incomplete datasets where a part of training samples have unobserved data. To address these issues, in this paper, we firstly propose a robust feature selectionframework to relieve the influence of outliers, andthen introduce an indicator matrix to avoid unobserved data to take participation in numerical computation of feature selection so that both our proposed feature selection framework and exiting feature selection frameworks are available to conductfeature selection on incomplete data sets. We further propose a new optimization algorithm to optimize the resulting objective function as well asprove our algorithm to converge fast. Experimental results on both real and artificial incompletedata sets demonstrated that our proposed methodoutperformed the feature selection methods undercomparison in terms of clustering performance.

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IJCAI Conference 2018 Conference Paper

Robust Graph Dimensionality Reduction

Xiaofeng Zhu
Cong Lei
Hao Yu
Yonggang Li
Jiangzhang Gan
Shichao Zhang

In this paper, we propose conducting Robust Graph Dimensionality Reduction (RGDR) by learning a transformation matrix to map original high-dimensional data into their low-dimensional intrinsic space without the influence of outliers. To do this, we propose simultaneously 1) adaptively learning three variables, \ie a reverse graph embedding of original data, a transformation matrix, and a graph matrix preserving the local similarity of original data in their low-dimensional intrinsic space; and 2) employing robust estimators to avoid outliers involving the processes of optimizing these three matrices. As a result, original data are cleaned by two strategies, \ie a prediction of original data based on three resulting variables and robust estimators, so that the transformation matrix can be learnt from accurately estimated intrinsic space with the helping of the reverse graph embedding and the graph matrix. Moreover, we propose a new optimization algorithm to the resulting objective function as well as theoretically prove the convergence of our optimization algorithm. Experimental results indicated that our proposed method outperformed all the comparison methods in terms of different classification tasks.

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IJCAI Conference 2018 Conference Paper

Robust Multi-view Learning via Half-quadratic Minimization

Yonghua Zhu
Xiaofeng Zhu
Wei Zheng

Although multi-view clustering is capable to usemore information than single view clustering, existing multi-view clustering methods still have issues to be addressed, such as initialization sensitivity, the specification of the number of clusters, and the influence of outliers. In this paper, we propose a robust multi-view clustering method to address these issues. Specifically, we first propose amulti-view based sum-of-square error estimation tomake the initialization easy and simple as well asuse a sum-of-norm regularization to automaticallylearn the number of clusters according to data distribution. We further employ robust estimators constructed by the half-quadratic theory to avoid theinfluence of outliers for conducting robust estimations of both sum-of-square error and the numberof clusters. Experimental results on both syntheticand real datasets demonstrate that our method outperforms the state-of-the-art methods.

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IJCAI Conference 2017 Conference Paper

Adaptive Hypergraph Learning for Unsupervised Feature Selection

Xiaofeng Zhu
Yonghua Zhu
Shichao Zhang
Rongyao Hu
Wei He

Current unsupervised feature selection (UFS) methods learn the similarity matrix by using a simple graph which is learnt from the original data as well as is independent from the process of feature selection, and thus unable to efficiently remove the redundant/irrelevant features. To address these issues, we propose a new UFS method to jointly learn the similarity matrix and conduct both subspace learning (via learning a dynamic hypergraph) and feature selection (via a sparsity constraint). As a result, we reduce the feature dimensions using different methods (i. e. , subspace learning and feature selection) from different feature spaces, and thus makes our method select the informative features effectively and robustly. We tested our method using benchmark datasets to conduct the clustering tasks using the selected features, and the experimental results show that our proposed method outperforms all the comparison methods.

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TIST Journal 2017 Journal Article

Learning k for kNN Classification

Shichao Zhang
Xuelong Li
Ming Zong
Xiaofeng Zhu
Debo Cheng

The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data points. Then, a graph Laplacian regularizer is advocated to preserve the local structure of the data in the reconstruction process. Moreover, an ℓ 1 -norm regularizer and an ℓ 2, 1 -norm regularizer are applied to learn different k values for different test data and to result in low sparsity to remove the redundant/noisy feature from the reconstruction process, respectively. Besides for classification tasks, the kNN methods (including our proposed CM-kNN method) are further utilized to regression and missing data imputation. We conducted sets of experiments for illustrating the efficiency, and experimental results showed that the proposed method was more accurate and efficient than existing kNN methods in data-mining applications, such as classification, regression, and missing data imputation.

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AAAI Conference 2017 Conference Paper

One-Step Spectral Clustering via Dynamically Learning Affinity Matrix and Subspace

Xiaofeng Zhu
Wei He
Yonggang Li
Yang Yang
Shichao Zhang
Rongyao Hu
Yonghua Zhu

This paper proposes a one-step spectral clustering method by learning an intrinsic afﬁnity matrix (i. e. , the clustering result) from the low-dimensional space (i. e. , intrinsic subspace) of original data. Speciﬁcally, the intrinsic afﬁnity matrix is learnt by: 1) the alignment of the initial afﬁnity matrix learnt from original data; 2) the adjustment of the transformation matrix, which transfers the original feature space into its intrinsic subspace by simultaneously conducting feature selection and subspace learning; and 3) the clustering result constraint, i. e. , the graph constructed by the intrinsic afﬁnity matrix has exact c connected components where c is the number of clusters. In this way, two afﬁnity matrices and a transformation matrix are iteratively updated until achieving their individual optimum, so that these two afﬁnity matrices are consistent and the intrinsic subspace is learnt via the transformation matrix. Experimental results on both synthetic and benchmark datasets veriﬁed that our proposed method outputted more effective clustering result than the previous clustering methods.

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YNIMG Journal 2014 Journal Article

A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis

Xiaofeng Zhu
Heung-Il Suk
Dinggang Shen

Recent studies on AD/MCI diagnosis have shown that the tasks of identifying brain disease and predicting clinical scores are highly related to each other. Furthermore, it has been shown that feature selection with a manifold learning or a sparse model can handle the problems of high feature dimensionality and small sample size. However, the tasks of clinical score regression and clinical label classification were often conducted separately in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i. e. , predictions of clinical scores and a class label. In order to validate the effectiveness of the proposed method, we conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function helped enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.

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