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Chong Peng

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11 papers

2 author rows

ICML Conference 2025 Conference Paper

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer

Haotian Ni
Yake Wei
Hang Liu
Gong Chen
Chong Peng
Hao Lin
Di Hu 0001

Multimodal learning faces challenges in effectively fusing information from diverse modalities, especially when modality quality varies across samples. Dynamic fusion strategies, such as attention mechanism in Transformers, aim to address such challenge by adaptively emphasizing modalities based on the characteristics of input data. However, through amounts of carefully designed experiments, we surprisingly observed that the dynamic adaptability of widely-used self-attention models diminishes. Model tends to prefer one modality regardless of data characteristics. This bias triggers a self-reinforcing cycle that progressively overemphasizes the favored modality, widening the distribution gap in attention keys across modalities and deactivating attention mechanism’s dynamic properties. To revive adaptability, we propose a simple yet effective method Rolling Query (RollingQ), which balances attention allocation by rotating the query to break the self-reinforcing cycle and mitigate the key distribution gap. Extensive experiments on various multimodal scenarios validate the effectiveness of RollingQ and the restoration of cooperation dynamics is pivotal for enhancing the broader capabilities of widely deployed multimodal Transformers. The source code is available at https: //github. com/GeWu-Lab/RollingQ_ICML2025.

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IJCAI Conference 2024 Conference Paper

Cross-View Diversity Embedded Consensus Learning for Multi-View Clustering

Chong Peng
Kai Zhang
Yongyong Chen
Chenglizhao Chen
Qiang Cheng

Multi-view clustering (MVC) has garnered significant attention in recent studies. In this paper, we propose a novel MVC method, named CCL-MVC. The novel method constructs a cross-order neighbor tensor of multi-view data to recover a low-rank essential tensor, preserves noise-free, comprehensive, and complementary cross-order relationships among the samples. Furthermore, it constructs a consensus representation matrix by fusing the low-rank essential tensor with auto-adjusted cross-view diversity embedding, fully exploiting both consensus and discriminative information of the data. An effective optimization algorithm is developed, which is theoretically guaranteed to converge. Extensive experimental results confirm the effectiveness of the proposed method.

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IJCAI Conference 2023 Conference Paper

Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels

Hao-Tian Li
Tong Wei
Hao Yang
Kun Hu
Chong Peng
Li-Bo Sun
Xun-Liang Cai
Min-Ling Zhang

Deep neural networks have shown promising results on a wide variety of tasks using large-scale and well-annotated training datasets. However, data collected from real-world applications can suffer from two prevalent biases, i. e. , long-tailed class distribution and label noise. Previous efforts on long-tailed learning and label-noise learning can only address a single type of data bias, leading to a severe deterioration of their performance. In this paper, we propose a distance-based sample selection algorithm called Stochastic Feature Averaging (SFA), which fits a Gaussian using the exponential running average of class centroids to capture uncertainty in representation space due to label noise and data scarcity. With SFA, we detect noisy samples based on their distances to class centroids sampled from this Gaussian distribution. Based on the identified clean samples, we then propose to train an auxiliary balanced classifier to improve the generalization for the minority class and facilitate the update of Gaussian parameters. Extensive experimental results show that SFA can enhance the performance of existing methods on both simulated and real-world datasets. Further, we propose to combine SFA with the sample-selection approach, distribution-robust, and noise-robust loss functions, resulting in significant improvement in performance over the baselines. Our code is available at https: //github. com/HotanLee/SFA

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AAAI Conference 2021 Conference Paper

Self-Paced Two-dimensional PCA

Jiangxin Li
Zhao Kang
Chong Peng
Wenyu Chen

Two-dimensional PCA (2DPCA) is an effective approach to reduce dimension and extract features in the image domain. Most recently developed techniques use different error measures to improve their robustness to outliers. When certain data points are overly contaminated, the existing methods are frequently incapable of filtering out and eliminating the excessively polluted ones. Moreover, natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static. Unlike previous studies, we explicitly differentiate the samples to alleviate the impact of outliers and propose a novel method called Self-Paced 2DPCA (SP2DPCA) algorithm, which progresses from ‘easy’ to ‘complex’ samples. By using an alternative optimization strategy, SP2DPCA looks for optimal projection matrix and filters out outliers iteratively. Theoretical analysis demonstrates the robustness nature of our method. Extensive experiments on image reconstruction and clustering verify the superiority of our approach.

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AAAI Conference 2020 Conference Paper

Social Influence Does Matter: User Action Prediction for In-Feed Advertising

Hongyang Wang
Qingfei Meng
Ju Fan
Yuchen Li
Laizhong Cui
Xiaoman Zhao
Chong Peng
Gong Chen

Social in-feed advertising delivers ads that seamlessly ﬁt inside a user’s feed, and allows users to engage in social actions (likes or comments) with the ads. Many businesses pay higher attention to “engagement marketing” that maximizes social actions, as social actions can effectively promote brand awareness. This paper studies social action prediction for infeed advertising. Most existing works overlook the social in- ﬂuence as a user’s action may be affected by her friends’ actions. This paper introduces an end-to-end approach that leverages social inﬂuence for action prediction, and focuses on addressing the high sparsity challenge for in-feed ads. We propose to learn inﬂuence structure that models who tends to be inﬂuenced. We extract a subgraph with the near neighbors a user interacts with, and learn topological features of the subgraph by developing structure-aware graph encoding methods. We also introduce graph attention networks to learn inﬂuence dynamics that models how a user is inﬂuenced by neighbors’ actions. We conduct extensive experiments on real datasets from the commercial advertising platform of WeChat and a public dataset. The experimental results demonstrate that social inﬂuence learned by our approach can signiﬁcantly boost performance of social action prediction.

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TIST Journal 2018 Journal Article

Integrate and Conquer

Chong Peng
Zhao Kang
Shuting Cai
Qiang Cheng

In this article, we introduce a novel, general methodology, called integrate and conquer, for simultaneously accomplishing the tasks of feature extraction, manifold construction, and clustering, which is taken to be superior to building a clustering method as a single task. When the proposed novel methodology is used on two-dimensional (2D) data, it naturally induces a new clustering method highly effective on 2D data. Existing clustering algorithms usually need to convert 2D data to vectors in a preprocessing step, which, unfortunately, severely damages 2D spatial information and omits inherent structures and correlations in the original data. The induced new clustering method can overcome the matrix-vectorization-related issues to enhance the clustering performance on 2D matrices. More specifically, the proposed methodology mutually enhances three tasks of finding subspaces, learning manifolds, and constructing data representation in a seamlessly integrated fashion. When used on 2D data, we seek two projection matrices with optimal numbers of directions to project the data into low-rank, noise-mitigated, and the most expressive subspaces, in which manifolds are adaptively updated according to the projections, and new data representation is built with respect to the projected data by accounting for nonlinearity via adaptive manifolds. Consequently, the learned subspaces and manifolds are clean and intrinsic, and the new data representation is discriminative and robust. Extensive experiments have been conducted and the results confirm the effectiveness of the proposed methodology and algorithm.

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AAAI Conference 2018 Conference Paper

Unified Spectral Clustering With Optimal Graph

Zhao Kang
Chong Peng
Qiang Cheng
Zenglin Xu

Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential ﬂaws, which may lead to severe information loss and performance degradation. First, predeﬁned similarity graph might not be optimal for subsequent clustering. It is well-accepted that similarity graph highly affects the clustering results. To this end, we propose to automatically learn similarity information from data and simultaneously consider the constraint that the similarity matrix has exact c connected components if there are c clusters. Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers. In this work, we transform the candidate solution into a new one that better approximates the discrete one. Finally, those three subtasks are integrated into a uniﬁed framework, with each subtask iteratively boosted by using the results of the others towards an overall optimal solution. It is known that the performance of a kernel method is largely determined by the choice of kernels. To tackle this practical problem of how to select the most suitable kernel for a particular data set, we further extend our model to incorporate multiple kernel learning ability. Extensive experiments demonstrate the superiority of our proposed method as compared to existing clustering approaches.

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TIST Journal 2017 Journal Article

Nonnegative Matrix Factorization with Integrated Graph and Feature Learning

Chong Peng
Zhao Kang
Yunhong Hu
Jie Cheng
Qiang Cheng

Matrix factorization is a useful technique for data representation in many data mining and machine learning tasks. Particularly, for data sets with all nonnegative entries, matrix factorization often requires that factor matrices be nonnegative, leading to nonnegative matrix factorization (NMF). One important application of NMF is for clustering with reduced dimensions of the data represented in the new feature space. In this paper, we propose a new graph regularized NMF method capable of feature learning and apply it to clustering. Unlike existing NMF methods that treat all features in the original feature space equally, our method distinguishes features by incorporating a feature-wise sparse approximation error matrix in the formulation. It enables important features to be more closely approximated by the factor matrices. Meanwhile, the graph of the data is constructed using cleaner features in the feature learning process, which integrates feature learning and manifold learning procedures into a unified NMF model. This distinctly differs from applying the existing graph-based NMF models after feature selection in that, when these two procedures are independently used, they often fail to align themselves toward obtaining a compact and most expressive data representation. Comprehensive experimental results demonstrate the effectiveness of the proposed method, which outperforms state-of-the-art algorithms when applied to clustering.

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AAAI Conference 2017 Conference Paper

Twin Learning for Similarity and Clustering: A Unified Kernel Approach

Zhao Kang
Chong Peng
Qiang Cheng

Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is challenging because it is usually impacted by many factors, e. g. , the choice of similarity metric, neighborhood size, scale of data, noise and outliers. Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering. In addition, nonlinear similarity often exists in many real world data which, however, has not been effectively considered by most existing methods. To tackle these two challenges, we propose a model to simultaneously learn cluster indicator matrix and similarity information in kernel spaces in a principled way. We show theoretical relationships to kernel k-means, k-means, and spectral clustering methods. Then, to address the practical issue of how to select the most suitable kernel for a particular clustering task, we further extend our model with a multiple kernel learning ability. With this joint model, we can automatically accomplish three subtasks of ﬁnding the best cluster indicator matrix, the most accurate similarity relations and the optimal combination of multiple kernels. By leveraging the interactions between these three subtasks in a joint framework, each subtask can be iteratively boosted by using the results of the others towards an overall optimal solution. Extensive experiments are performed to demonstrate the effectiveness of our method.

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TIST Journal 2016 Journal Article

A Supervised Learning Model for High-Dimensional and Large-Scale Data

Chong Peng
Jie Cheng
Qiang Cheng

We introduce a new supervised learning model using a discriminative regression approach. This new model estimates a regression vector to represent the similarity between a test example and training examples while seamlessly integrating the class information in the similarity estimation. This distinguishes our model from usual regression models and locally linear embedding approaches, rendering our method suitable for supervised learning problems in high-dimensional settings. Our model is easily extensible to account for nonlinear relationship and applicable to general data, including both high- and low-dimensional data. The objective function of the model is convex, for which two optimization algorithms are provided. These two optimization approaches induce two scalable solvers that are of mathematically provable, linear time complexity. Experimental results verify the effectiveness of the proposed method on various kinds of data. For example, our method shows comparable performance on low-dimensional data and superior performance on high-dimensional data to several widely used classifiers; also, the linear solvers obtain promising performance on large-scale classification.

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AAAI Conference 2016 Conference Paper

Top-N Recommender System via Matrix Completion

Zhao Kang
Chong Peng
Qiang Cheng

Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We ﬁll the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efﬁcient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.

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