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Xiaojie Guo

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AAAI Conference 2026 Conference Paper

Semi-Supervised Semantic Segmentation via Derivative Label Propagation

  • Yuanbin Fu
  • Xiaojie Guo

Semi-supervised semantic segmentation, which leverages a limited set of labeled images, helps to relieve the heavy annotation burden. While pseudo-labeling strategies yield promising results, there is still room for enhancing the reliability of pseudo-labels. Hence, we develop a semi-supervised framework, namely DerProp, equipped with a novel derivative label propagation to rectify imperfect pseudo-labels. Our label propagation method imposes discrete derivative operations on pixel-wise feature vectors as additional regularization, thereby generating strictly regularized similarity metrics. Doing so effectively alleviates the ill-posed problem that identical similarities correspond to different features, through constraining the solution space. Extensive experiments are conducted to verify the rationality of our design, and demonstrate our superiority over other methods.

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AAAI Conference 2025 Conference Paper

Differential Alignment for Domain Adaptive Object Detection

  • Xinyu He
  • Xinhui Li
  • Xiaojie Guo

Domain adaptive object detection (DAOD) aims to generalize an object detector trained on labeled source-domain data to a target domain without annotations, the core principle of which is source-target feature alignment. Typically, existing approaches employ adversarial learning to align the distributions of the source and target domains as a whole, barely considering the varying significance of distinct regions, say instances under different circumstances and foreground vs background areas, during feature alignment. To overcome the shortcoming, we investigate a differential feature alignment strategy. Specifically, a prediction-discrepancy feedback instance alignment module (dubbed PDFA) is designed to adaptively assign higher weights to instances of higher teacher-student detection discrepancy, effectively handling heavier domain-specific information. Additionally, an uncertainty-based foreground-oriented image alignment module (UFOA) is proposed to explicitly guide the model to focus more on regions of interest. Extensive experiments on widely-used DAOD datasets together with ablation studies are conducted to demonstrate the efficacy of our proposed method and reveal its superiority over other SOTA alternatives.

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NeurIPS Conference 2025 Conference Paper

HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations

  • Shuaicheng Zhang
  • Haohui Wang
  • Junhong Lin
  • Xiaojie Guo
  • Yada Zhu
  • Si Zhang
  • Dongqi Fu
  • Dawei Zhou

Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach - using low-pass filters for homophilic graphs and high-pass filters for heterophilic graphs. However, we discover that the relationship between graph heterophily and spectral filters is more complex - the optimal filter response varies across frequency components and does not follow a strict monotonic correlation with heterophily degree. This finding challenges conventional fixed filter designs and suggests the need for adaptive filtering to preserve expressiveness in graph embeddings. Formally, natural questions arise: Given a heterophilic graph $\mathcal{G}$, how and to what extent will the varying heterophily degree of $\mathcal{G}$ affect the performance of GNNs? How can we design adaptive filters to fit those varying heterophilic connections? Our theoretical analysis reveals that the average frequency response of GNNs and graph heterophily degree do not follow a strict monotonic correlation, necessitating adaptive graph filters to guarantee good generalization performance. Hence, we propose HeroFilter, a simple yet powerful GNN, which extracts information across the heterophily spectrum and combines salient representations through adaptive mixing. HeroFilter's superior performance achieves up to 9. 2% accuracy improvement over leading baselines across homophilic and heterophilic graphs.

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NeurIPS Conference 2025 Conference Paper

MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery

  • Hainuo Wang
  • Qiming Hu
  • Xiaojie Guo

Restoring images degraded by adverse weather remains a significant challenge due to the highly non-uniform and spatially heterogeneous nature of weather-induced artifacts, \emph{e. g. }, fine-grained rain streaks versus widespread haze. Accurately estimating the underlying degradation can intuitively provide restoration models with more targeted and effective guidance, enabling adaptive processing strategies. To this end, we propose a Morton-Order Degradation Estimation Mechanism (MODEM) for adverse weather image restoration. Central to MODEM is the Morton-Order 2D-Selective-Scan Module (MOS2D), which integrates Morton-coded spatial ordering with selective state-space models to capture long-range dependencies while preserving local structural coherence. Complementing MOS2D, we introduce a Dual Degradation Estimation Module (DDEM) that disentangles and estimates both global and local degradation priors. These priors dynamically condition the MOS2D modules, facilitating adaptive and context-aware restoration. Extensive experiments and ablation studies demonstrate that MODEM achieves state-of-the-art results across multiple benchmarks and weather types, highlighting its effectiveness in modeling complex degradation dynamics. Our code will be released soon.

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JMLR Journal 2025 Journal Article

Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis

  • Hongru Yang
  • Yingbin Liang
  • Xiaojie Guo
  • Lingfei Wu
  • Zhangyang Wang

It has been observed that applying pruning-at-initialization methods and training the sparse networks can sometimes yield slightly better test performance than training the original dense network. Such experimental observations are yet to be understood theoretically. This work makes the first attempt to study this phenomenon. Specifically, we identify a theoretical minimal setting and study a classification task with a one-hidden-layer neural network, which is randomly pruned according to different rates at the initialization. We show that as long as the pruning rate is below a certain threshold, the network provably exhibits good generalization performance after training.More surprisingly, the generalization bound gets better as the pruning rate mildly gets larger. To complement this positive result, we also show a negative result: there exists a large pruning rate such that while gradient descent is still able to drive the training loss toward zero, the generalization performance is no better than random guessing. This further suggests that pruning can change the feature learning process, which leads to the performance drop of the pruned neural network. To our knowledge, this is the first theory work studying how different pruning rates affect neural networks' performance, suggesting that an appropriate pruning rate might improve the neural network's generalization. [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

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NeurIPS Conference 2025 Conference Paper

Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders

  • Qiming Hu
  • Linlong Fan
  • Yiyan Luo
  • Yuhang Yu
  • Xiaojie Guo
  • Qingnan Fan

The introduction of generative models has significantly advanced image super-resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues, particularly distorting textual structures. In this paper, we introduce a novel diffusion-based SR framework, namely TADiSR, which integrates text-aware attention and joint segmentation decoders to recover not only natural details but also the structural fidelity of text regions in degraded real-world images. Moreover, we propose a complete pipeline for synthesizing high-quality images with fine-grained full-image text masks, combining realistic foreground text regions with detailed background content. Extensive experiments demonstrate that our approach substantially enhances text legibility in super-resolved images, achieving state-of-the-art performance across multiple evaluation metrics and exhibiting strong generalization to real-world scenarios. Our code is available at here.

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NeurIPS Conference 2024 Conference Paper

Single Image Reflection Separation via Dual-Stream Interactive Transformers

  • Qiming Hu
  • Hainuo Wang
  • Xiaojie Guo

Despite satisfactory results on ``easy'' cases of single image reflection separation, prior dual-stream methods still suffer from considerable performance degradation when facing complex ones, i. e, the transmission layer is densely entangled with the reflection having a wide distribution of spatial intensity. The main reasons come from the lack of concern on the feature correlation during interaction, and the limited receptive field. To remedy these deficiencies, this paper presents a Dual-Stream Interactive Transformer (DSIT) design. Specifically, we devise a dual-attention interactive structure that embraces a dual-stream self-attention and a layer-aware dual-stream cross-attention mechanism to simultaneously capture intra-layer and inter-layer feature correlations. Meanwhile, the introduction of attention mechanisms can also mitigate the receptive field limitation. We modulate single-stream pre-trained Transformer embeddings with dual-stream convolutional features through cross-architecture interactions to provide richer semantic priors, thereby further relieving the ill-posedness of the problem. Extensive experimental results reveal the merits of the proposed DSIT over other state-of-the-art alternatives. Our code is publicly available at https: //github. com/mingcv/DSIT.

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AAAI Conference 2023 Conference Paper

Adaptive Texture Filtering for Single-Domain Generalized Segmentation

  • Xinhui Li
  • Mingjia Li
  • Yaxing Wang
  • Chuan-Xian Ren
  • Xiaojie Guo

Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain-specific features. However, these approaches depends heavily on the richness of the texture bank and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve the generalization. In this paper, we present a novel adaptive texture filtering mechanism to suppress the influence of texture without using augmentation, thus eliminating the interference of domain-specific features. Further, we design a hierarchical guidance generalization network equipped with structure-guided enhancement modules, which purpose to learn the domain-invariant generalized knowledge. Extensive experiments together with ablation studies on widely-used datasets are conducted to verify the effectiveness of the proposed model, and reveal its superiority over other state-of-the-art alternatives.

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AAAI Conference 2023 Conference Paper

YOLOV: Making Still Image Object Detectors Great at Video Object Detection

  • Yuheng Shi
  • Naiyan Wang
  • Xiaojie Guo

Video object detection (VID) is challenging because of the high variation of object appearance as well as the diverse deterioration in some frames. On the positive side, the detection in a certain frame of a video, compared with that in a still image, can draw support from other frames. Hence, how to aggregate features across different frames is pivotal to VID problem. Most of existing aggregation algorithms are customized for two-stage detectors. However, these detectors are usually computationally expensive due to their two-stage nature. This work proposes a simple yet effective strategy to address the above concerns, which costs marginal overheads with significant gains in accuracy. Concretely, different from traditional two-stage pipeline, we select important regions after the one-stage detection to avoid processing massive low-quality candidates. Besides, we evaluate the relationship between a target frame and reference frames to guide the aggregation. We conduct extensive experiments and ablation studies to verify the efficacy of our design, and reveal its superiority over other state-of-the-art VID approaches in both effectiveness and efficiency. Our YOLOX-based model can achieve promising performance (e.g., 87.5% AP50 at over 30 FPS on the ImageNet VID dataset on a single 2080Ti GPU), making it attractive for large-scale or real-time applications. The implementation is simple, we have made the demo codes and models available at https://github.com/YuHengsss/YOLOV.

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NeurIPS Conference 2022 Conference Paper

Deep Generative Model for Periodic Graphs

  • Shiyu Wang
  • Xiaojie Guo
  • Liang Zhao

Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and polygon mesh. Their generative modeling has great potential in real-world applications such as material design and graphics synthesis. Classical models either rely on domain-specific predefined generation principles (e. g. , in crystal net design), or follow geometry-based prescribed rules. Recently, deep generative models have shown great promise in automatically generating general graphs. However, their advancement into periodic graphs has not been well explored due to several key challenges in 1) maintaining graph periodicity; 2) disentangling local and global patterns; and 3) efficiency in learning repetitive patterns. To address them, this paper proposes Periodical-Graph Disentangled Variational Auto-encoder (PGD-VAE), a new deep generative model for periodic graphs that can automatically learn, disentangle, and generate local and global graph patterns. Specifically, we develop a new periodic graph encoder consisting of global-pattern encoder and local-pattern encoder that ensures to disentangle the representation into global and local semantics. We then propose a new periodic graph decoder consisting of local structure decoder, neighborhood decoder, and global structure decoder, as well as the assembler of their outputs that guarantees periodicity. Moreover, we design a new model learning objective that helps ensure the invariance of local-semantic representations for the graphs with the same local structure. Comprehensive experimental evaluations have been conducted to demonstrate the effectiveness of the proposed method.

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AAAI Conference 2022 Conference Paper

Disentangled Spatiotemporal Graph Generative Models

  • Yuanqi Du
  • Xiaojie Guo
  • Hengning Cao
  • Yanfang Ye
  • Liang Zhao

Spatiotemporal graph represents a crucial data structure where the nodes and edges are embedded in a geometric space and can evolve dynamically over time. Nowadays, spatiotemporal graph data is becoming increasingly popular and important, ranging from microscale (e. g. protein folding), to middle-scale (e. g. dynamic functional connectivity), to macro-scale (e. g. human mobility network). Although disentangling and understanding the correlations among spatial, temporal, and graph aspects have been a long-standing key topic in network science, they typically rely on network processing hypothesized by human knowledge. This usually fit well towards the graph properties which can be predefined, but cannot do well for the most cases, especially for many key domains where the human has yet very limited knowledge such as protein folding and biological neuronal networks. In this paper, we aim at pushing forward the modeling and understanding of spatiotemporal graphs via new disentangled deep generative models. Specifically, a new Bayesian model is proposed that factorizes spatiotemporal graphs into spatial, temporal, and graph factors as well as the factors that explain the interplay among them. A variational objective function and new mutual information thresholding algorithms driven by information bottleneck theory have been proposed to maximize the disentanglement among the factors with theoretical guarantees. Qualitative and quantitative experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed model over the state-of-the-arts by up to 69. 2% for graph generation and 41. 5% for interpretability.

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NeurIPS Conference 2022 Conference Paper

Multi-objective Deep Data Generation with Correlated Property Control

  • Shiyu Wang
  • Xiaojie Guo
  • Xuanyang Lin
  • Bo Pan
  • Yuanqi Du
  • Yinkai Wang
  • Yanfang Ye
  • Ashley Petersen

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design. However, the advance of deep generative models is limited by the challenges to generate objects that possess multiple desired properties because: 1) the existence of complex correlation among real-world properties is common but hard to identify; 2) controlling individual property enforces an implicit partially control of its correlated properties, which is difficult to model; 3) controlling multiple properties under variour manners simultaneously is hard and underexplored. We address these challenges by proposing a novel deep generative framework that recovers semantics and correlation of properties through disentangled latent vectors. The correlation is handled via an explainable mask pooling layer, and properties are precisely retained by the generated objects via the mutual dependence between latent vectors and properties. Our generative model preserves properties of interest while handles correlation and conflicts of properties under a multi-objective optimization framework. The experiments demonstrate our model's superior performance in generating objects with desired properties.

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IJCAI Conference 2022 Conference Paper

Multi-scale Spatial Representation Learning via Recursive Hermite Polynomial Networks

  • Lin (Yuanbo) Wu
  • Deyin Liu
  • Xiaojie Guo
  • Richang Hong
  • Liangchen Liu
  • Rui Zhang

Multi-scale representation learning aims to leverage diverse features from different layers of Convolutional Neural Networks (CNNs) for boosting the feature robustness to scale variance. For dense prediction tasks, two key properties should be satisfied: the high spatial variance across convolutional layers, and the sub-scale granularity inside a convolutional layer for fine-grained features. To pursue the two properties, this paper proposes Recursive Hermite Polynomial Networks (RHP-Nets for short). The proposed RHP-Nets consist of two major components: 1) a dilated convolution to maintain the spatial resolution across layers, and 2) a family of Hermite polynomials over a subset of dilated grids, which recursively constructs sub-scale representations to avoid the artifacts caused by naively applying the dilation convolution. The resultant sub-scale granular features are fused via trainable Hermite coefficients to form the multi-resolution representations that can be fed into the next deeper layer, and thus allowing feature interchanging at all levels. Extensive experiments are conducted to demonstrate the efficacy of our design, and reveal its superiority over state-of-the-art alternatives on a variety of image recognition tasks. Besides, introspective studies are provided to further understand the properties of our method.

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NeurIPS Conference 2021 Conference Paper

GraphGT: Machine Learning Datasets for Graph Generation and Transformation

  • Yuanqi Du
  • Shiyu Wang
  • Xiaojie Guo
  • Hengning Cao
  • Shujie Hu
  • Junji Jiang
  • Aishwarya Varala
  • Abhinav Angirekula

Graph generation has shown great potential in applications like network design and mobility synthesis and is one of the fastest-growing domains in machine learning for graphs. Despite the success of graph generation, the corresponding real-world datasets are few and limited to areas such as molecules and citation networks. To fill the gap, we introduce GraphGT, a large dataset collection for graph generation and transformation problem, which contains 36 datasets from 9 domains across 6 subjects. To assist the researchers with better explorations of the datasets, we provide a systemic review and classification of the datasets based on research tasks, graph types, and application domains. We have significantly (re)processed all the data from different domains to fit the unified framework of graph generation and transformation problems. In addition, GraphGT provides an easy-to-use graph generation pipeline that simplifies the process for graph data loading, experimental setup and model evaluation. Finally, we compare the performance of popular graph generative models in 16 graph generation and 17 graph transformation datasets, showing the great power of GraphGT in differentiating and evaluating model capabilities and drawbacks. GraphGT has been regularly updated and welcomes inputs from the community. GraphGT is publicly available at \url{https: //graphgt. github. io/} and can also be accessed via an open Python library.

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NeurIPS Conference 2021 Conference Paper

Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation

  • Qiming Hu
  • Xiaojie Guo

Single image reflection separation (SIRS), as a representative blind source separation task, aims to recover two layers, $\textit{i. e. }$, transmission and reflection, from one mixed observation, which is challenging due to the highly ill-posed nature. Existing deep learning based solutions typically restore the target layers individually, or with some concerns at the end of the output, barely taking into account the interaction across the two streams/branches. In order to utilize information more efficiently, this work presents a general yet simple interactive strategy, namely $\textit{your trash is my treasure}$ (YTMT), for constructing dual-stream decomposition networks. To be specific, we explicitly enforce the two streams to communicate with each other block-wisely. Inspired by the additive property between the two components, the interactive path can be easily built via transferring, instead of discarding, deactivated information by the ReLU rectifier from one stream to the other. Both ablation studies and experimental results on widely-used SIRS datasets are conducted to demonstrate the efficacy of YTMT, and reveal its superiority over other state-of-the-art alternatives. The implementation is quite simple and our code is publicly available at https: //github. com/mingcv/YTMT-Strategy.

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AAAI Conference 2020 Conference Paper

FusionDN: A Unified Densely Connected Network for Image Fusion

  • Han Xu
  • Jiayi Ma
  • Zhuliang Le
  • Junjun Jiang
  • Xiaojie Guo

In this paper, we present a new unsupervised and unified densely connected network for different types of image fusion tasks, termed as FusionDN. In our method, the densely connected network is trained to generate the fused image conditioned on source images. Meanwhile, a weight block is applied to obtain two data-driven weights as the retention degrees of features in different source images, which are the measurement of the quality and the amount of information in them. Losses of similarities based on these weights are applied for unsupervised learning. In addition, we obtain a single model applicable to multiple fusion tasks by applying elastic weight consolidation to avoid forgetting what has been learned from previous tasks when training multiple tasks sequentially, rather than train individual models for every fusion task or jointly train tasks roughly. Qualitative and quantitative results demonstrate the advantages of FusionDN compared with state-of-the-art methods in different fusion tasks.

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AAAI Conference 2020 Conference Paper

Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity

  • Hao Zhang
  • Han Xu
  • Yang Xiao
  • Xiaojie Guo
  • Jiayi Ma

In this paper, we propose a fast unified image fusion network based on proportional maintenance of gradient and intensity (PMGI), which can end-to-end realize a variety of image fusion tasks, including infrared and visible image fusion, multiexposure image fusion, medical image fusion, multi-focus image fusion and pan-sharpening. We unify the image fusion problem into the texture and intensity proportional maintenance problem of the source images. On the one hand, the network is divided into gradient path and intensity path for information extraction. We perform feature reuse in the same path to avoid loss of information due to convolution. At the same time, we introduce the pathwise transfer block to exchange information between different paths, which can not only pre-fuse the gradient information and intensity information, but also enhance the information to be processed later. On the other hand, we define a uniform form of loss function based on these two kinds of information, which can adapt to different fusion tasks. Experiments on publicly available datasets demonstrate the superiority of our PMGI over the state-of-the-art in terms of both visual effect and quantitative metric in a variety of fusion tasks. In addition, our method is faster compared with the state-of-the-art.

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AAAI Conference 2018 Conference Paper

Distant-Supervision of Heterogeneous Multitask Learning for Social Event Forecasting With Multilingual Indicators

  • Liang Zhao
  • Junxiang Wang
  • Xiaojie Guo

Open-source indicators such as social media can be very effective precursors for forecasting future societal events. As events are often preceded by social indicators generated by groups of people speaking many different languages, multiple languages need to be considered to ensure comprehensive event forecasting. However, this leads to several technical challenges for traditional models: 1) high dimension, sparsity, and redundancy of features; 2) translation correlation among the multilingual features. and 3) lack of language-wise supervision. In order to simultaneously address these issues, we present a novel model capable of distant-supervision of heterogeneous multitask learning (DHML) for multilingual spatial social event forecasting. This model maps the multilingual heterogeneous features into several latent semantic spaces and then enforces a similar sparsity pattern across them all, using distant supervision across all the languages involved. Optimizing this model creates a difficult problem that is nonconvex and nonsmooth that can then be decomposed into simpler subproblems using the Alternative Direction Multiplier of Methods (ADMM). A novel dynamic programming-based algorithm is proposed to solve one challenging subproblem efficiently. Theoretical properties of the proposed algorithm are analyzed. The results of extensive experiments on multiple real-world datasets are presented to demonstrate the effectiveness, efficiency, and interpretability of the proposed approach.

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IJCAI Conference 2018 Conference Paper

Ensemble Soft-Margin Softmax Loss for Image Classification

  • Xiaobo Wang
  • Shifeng Zhang
  • Zhen Lei
  • Si Liu
  • Xiaojie Guo
  • Stan Z. Li

Softmax loss is arguably one of the most popular losses to train CNN models for image classification. However, recent works have exposed its limitation on feature discriminability. This paper casts a new viewpoint on the weakness of softmax loss. On the one hand, the CNN features learned using the softmax loss are often inadequately discriminative. We hence introduce a soft-margin softmax function to explicitly encourage the discrmination between different classes. On the other hand, the learned classifier of softmax loss is weak. We propose to assemble multiple these weak classifiers to a strong one, inspired by the recognition that the diversity among weak classifiers is critical to a good ensemble. To achieve the diversity, we adopt the Hilbert-Schmidt Independence Criterion (HSIC). Considering these two aspects in one framework, we design a novel loss, named as Ensemble Soft-Margin Softmax (EM-Softmax). Extensive experiments on benchmark datasets are conducted to show the superiority of our design over the baseline softmax loss and several state-of-the-art alternatives.

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IJCAI Conference 2017 Conference Paper

Exclusivity Regularized Machine: A New Ensemble SVM Classifier

  • Xiaojie Guo
  • Xiaobo Wang
  • Haibin Ling

The diversity of base learners is of utmost importance to a good ensemble. This paper defines a novel measurement of diversity, termed as exclusivity. With the designed exclusivity, we further propose an ensemble SVM classifier, namely Exclusivity Regularized Machine (ExRM), to jointly suppress the training error of ensemble and enhance the diversity between bases. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to effectively and efficiently seek the optimal solution of ExRM. Theoretical analysis on convergence, global optimality and linear complexity of the proposed algorithm, as well as experiments are provided to reveal the efficacy of our method and show its superiority over state-of-the-arts in terms of accuracy and efficiency.

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IJCAI Conference 2017 Conference Paper

ROUTE: Robust Outlier Estimation for Low Rank Matrix Recovery

  • Xiaojie Guo
  • Zhouchen Lin

In practice, even very high-dimensional data are typically sampled from low-dimensional subspaces but with intrusion of outliers and/or noises. Recovering the underlying structure and the pollution from the observations is key to understanding and processing such data. Besides properly modeling the low-rank structure of subspace, how to handle the pollution, is core regarding the performance of recovery. Often, the observed data is posed as a superimposition of the clean data and residual, while the residual can be roughly divided into two groups, including small dense noises and gross sparse outliers. Compared with small noises, outliers more likely ruin the recovery, as they can be arbitrarily large. By considering the above, this paper designs a method for recovering the low rank matrix with robust outlier estimation, termed as ROUTE, in a unified manner. Theoretical analysis on convergence and optimality, and experimental results on both synthetic and real data are provided to demonstrate the efficacy of our proposed method and show its superiority over other state-of-the-arts.

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IJCAI Conference 2015 Conference Paper

Online Robust Low Rank Matrix Recovery

  • Xiaojie Guo

Low rank matrix recovery has shown its importance as a theoretic foundation in many areas of information processing. Its solutions are usually obtained in batch mode that requires to load all the data into memory during processing, and thus are hardly applicable on large scale data. Moreover, a fraction of data may be severely contaminated by outliers, which makes accurate recovery significantly more challenging. This paper proposes a novel online robust low rank matrix recovery method to address these difficulties. In particular, we first introduce an online algorithm to solve the problem of low rank matrix completion. Then we move on to low rank matrix recovery from observations with intensive outliers. The outlier support is robustly estimated from a perspective of mixture model. Experiments on both synthetic and real data are conducted to demonstrate the efficacy of our method and show its superior performance over the state-of-the-arts.

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IJCAI Conference 2015 Conference Paper

Robust Subspace Segmentation by Simultaneously Learning Data Representations and Their Affinity Matrix

  • Xiaojie Guo

The goal of subspace segmentation is to partition a set of data drawn from a union of subspace into their underlying subspaces. The performance of spectral clustering based approaches heavily depends on learned data affinity matrices, which are usually constructed either directly from the raw data or from their computed representations. In this paper, we propose a novel method to simultaneously learn the representations of data and the affinity matrix of representation in a unified optimization framework. A novel Augmented Lagrangian Multiplier based algorithm is designed to effectively and efficiently seek the optimal solution of the problem. The experimental results on both synthetic and real data demonstrate the efficacy of the proposed method and its superior performance over the state-of-the-art alternatives.

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