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学者姓名：郑清海
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Abstract ：
Multi-view clustering has attracted significant attention in recent years because it can leverage the consistent and complementary information of multiple views to improve clustering performance. However, effectively fuse the information and balance the consistent and complementary information of multiple views are common challenges faced by multi-view clustering. Most existing multi-view fusion works focus on weighted-sum fusion and concatenating fusion, which unable to fully fuse the underlying information, and not consider balancing the consistent and complementary information of multiple views. To this end, we propose Cross-view Fusion for Multi-view Clustering (CFMVC). Specifically, CFMVC combines deep neural network and graph convolutional network for cross-view information fusion, which fully fuses feature information and structural information of multiple views. In order to balance the consistent and complementary information of multiple views, CFMVC enhances the correlation among the same samples to maximize the consistent information while simultaneously reinforcing the independence among different samples to maximize the complementary information. Experimental results on several multi-view datasets demonstrate the effectiveness of CFMVC for multi-view clustering task.
Keyword ：
Cross-view Cross-view deep neural network deep neural network graph convolutional network graph convolutional network multi-view clustering multi-view clustering multi-view fusion multi-view fusion
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| GB/T 7714 | Huang, Zhijie , Huang, Binqiang , Zheng, Qinghai et al. Cross-View Fusion for Multi-View Clustering [J]. | IEEE SIGNAL PROCESSING LETTERS , 2025 , 32 : 621-625 . |
| MLA | Huang, Zhijie et al. "Cross-View Fusion for Multi-View Clustering" . | IEEE SIGNAL PROCESSING LETTERS 32 (2025) : 621-625 . |
| APA | Huang, Zhijie , Huang, Binqiang , Zheng, Qinghai , Yu, Yuanlong . Cross-View Fusion for Multi-View Clustering . | IEEE SIGNAL PROCESSING LETTERS , 2025 , 32 , 621-625 . |
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Multi-view Representation Learning (MRL) has recently attracted widespread attention because it can integrate information from diverse data sources to achieve better performance. However, existing MRL methods still have two issues: (1) They typically perform various consistency objectives within the feature space, which might discard complementary information contained in each view. (2) Some methods only focus on handling inter-view relationships while ignoring inter-sample relationships that are also valuable for downstream tasks. To address these issues, we propose a novel Multi-view representation learning method with Dual-label Collaborative Guidance (MDCG). Specifically, we fully excavate and utilize valuable semantic and graph information hidden in multi-view data to collaboratively guide the learning process of MRL. By learning consistent semantic labels from distinct views, our method enhances intrinsic connections across views while preserving view-specific information, which contributes to learning the consistent and complementary unified representation. Moreover, we integrate similarity matrices of multiple views to construct graph labels that indicate inter-sample relationships. With the idea of self-supervised contrastive learning, graph structure information implied in graph labels is effectively captured by the unified representation, thus enhancing its discriminability. Extensive experiments on diverse real-world datasets demonstrate the effectiveness and superiority of MDCG compared with nine state-of-the-art methods. Our code will be available at https: //github.com/Bin1Chen/MDCG.
Keyword ：
Contrastive learning Contrastive learning Graph information Graph information Multi-view representation learning Multi-view representation learning Semantic information Semantic information
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| GB/T 7714 | Chen, Bin , Ren, Xiaojin , Bai, Shunshun et al. Multi-view representation learning with dual-label collaborative guidance [J]. | KNOWLEDGE-BASED SYSTEMS , 2024 , 305 . |
| MLA | Chen, Bin et al. "Multi-view representation learning with dual-label collaborative guidance" . | KNOWLEDGE-BASED SYSTEMS 305 (2024) . |
| APA | Chen, Bin , Ren, Xiaojin , Bai, Shunshun , Chen, Ziyuan , Zheng, Qinghai , Zhu, Jihua . Multi-view representation learning with dual-label collaborative guidance . | KNOWLEDGE-BASED SYSTEMS , 2024 , 305 . |
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Federated learning encounters substantial challenges with heterogeneous data, leading to performance degradation and convergence issues. While considerable progress has been achieved in mitigating such an impact, the reliability aspect of federated models has been largely disregarded. In this study, we conduct extensive experiments to investigate the reliability of both generic and personalized federated models. Our exploration uncovers a significant finding: federated models exhibit unreliability when faced with heterogeneous data, demonstrating poor calibration on in-distribution test data and low uncertainty levels on out-of-distribution data. This unreliability is primarily attributed to the presence of biased projection heads, which introduce miscalibration into the federated models. Inspired by this observation, we propose the "Assembled Projection Heads" (APH) method for enhancing the reliability of federated models. By treating the existing projection head parameters as priors, APH randomly samples multiple initialized parameters of projection heads from the prior and further performs targeted fine-tuning on locally available data under varying learning rates. Such a head ensemble introduces parameter diversity into the deterministic model, eliminating the bias and producing reliable predictions via head averaging. We evaluate the effectiveness of the proposed APH method across three prominent federated benchmarks. Experimental results validate the efficacy of APH in model calibration and uncertainty estimation. Notably, APH can be seamlessly integrated into various federated approaches but only requires less than 30% additional computation cost for 100x inferences within large models.
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| GB/T 7714 | Chen, Jinqian , Zhu, Jihua , Zheng, Qinghai et al. Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models [J]. | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 10 , 2024 : 11329-11337 . |
| MLA | Chen, Jinqian et al. "Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models" . | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 10 (2024) : 11329-11337 . |
| APA | Chen, Jinqian , Zhu, Jihua , Zheng, Qinghai , Li, Zhongyu , Tian, Zhiqiang . Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models . | THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 10 , 2024 , 11329-11337 . |
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Incomplete multi-view clustering is an important and challenging task, which has attracted significant attention in recent years. The key objective of incomplete multi-view clustering is to excavate the underlying avaliable consistency of multi-view data, so as to enable the effective reconstruction of missing views for clustering. In this paper, we introduce a completion framework that deeply explores the underlying consistency and effectively completes the missing views. Following that, we propose a novel Twin Reciprocal Completion for Incomplete multi-view clustering, termed TRC-IMC for short. To be specific, TRC-IMC jointly conducts the Completion in Feature space (CF) and the Completion in Subspace (CS) to reciprocally complete the data with missing views. The underlying high-order consistency of multi-view data can be fully explored in both the feature space and subspace to guide the completion process of missing views. Extensive experiments are conducted on eight real-world multi-view datasets, and experimental results indicate the promising performance of our method, compared to several state-of-the-arts.
Keyword ：
Circuits and systems Circuits and systems Clustering methods Clustering methods Excavation Excavation Incomplete multi-view data Incomplete multi-view data Kernel Kernel low-rank tensor constraint low-rank tensor constraint subspace clustering subspace clustering Task analysis Task analysis Tensors Tensors Vectors Vectors
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| GB/T 7714 | Zheng, Qinghai , Tang, Haoyu . Twin Reciprocal Completion for Incomplete Multi-View Clustering [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (12) : 13201-13212 . |
| MLA | Zheng, Qinghai et al. "Twin Reciprocal Completion for Incomplete Multi-View Clustering" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 12 (2024) : 13201-13212 . |
| APA | Zheng, Qinghai , Tang, Haoyu . Twin Reciprocal Completion for Incomplete Multi-View Clustering . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (12) , 13201-13212 . |
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Abstract ：
Recently, multi-view clustering methods have garnered considerable attention and have been applied in various domains. However, in practical scenarios, some samples may lack specific views, giving rise to the challenge of incomplete multi-view clustering. While some methods focus on completing missing data, incorrect completion can negatively affect representation learning. Moreover, separating completion and representation learning prevents the attainment of an optimal representation. Other methods eschew completion but singularly concentrate on either feature information or graph information, thus failing to achieve comprehensive representations. To address these challenges, we propose a graph-guided, imputation-free method for incomplete multi-view clustering. Unlike completion-based methods, our approach aims to maximize the utilization of existing information by simultaneously considering feature and graph information. This is realized through the feature learning component and the graph learning component. Introducing a degradation network, the former reconstructs view-specific representations proximate to available samples from a unified representation, seamlessly integrating feature information into the unified representation. Leveraging the semi-supervised idea, the latter utilizes reliable graph information from available samples to guide the learning of the unified representation. These two components collaborate to acquire a comprehensive unified representation for multi-view clustering. Extensive experiments conducted on real datasets demonstrate the effectiveness and competitiveness of the proposed method when compared with other state-of-the-art methods. Our code will be released on https://github.com/yff-java/GIMVC/. © 2024 Elsevier Ltd
Keyword ：
Graph information Graph information Incomplete multi-view clustering Incomplete multi-view clustering Representation learning Representation learning
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| GB/T 7714 | Bai, S. , Zheng, Q. , Ren, X. et al. Graph-guided imputation-free incomplete multi-view clustering [J]. | Expert Systems with Applications , 2024 , 258 . |
| MLA | Bai, S. et al. "Graph-guided imputation-free incomplete multi-view clustering" . | Expert Systems with Applications 258 (2024) . |
| APA | Bai, S. , Zheng, Q. , Ren, X. , Zhu, J. . Graph-guided imputation-free incomplete multi-view clustering . | Expert Systems with Applications , 2024 , 258 . |
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Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label distribution. Existing LE work typically focuses on primarily formulating a projection between feature space and label distribution space from discriminative model perspective, which preserves the relevance consistency that the sign of recovered label distribution should be consistent with the logical label. Different from previous algorithms, we formulate this problem from a causal perspective and present a novel LE method via the structured causal model (LESCM). Specifically, the proposed LESCM deliberates establishing the causal graph with assuming that label distribution is a cause of feature and logical label, which naturally satisfies the definition of label distribution learning (LDL). With capturing the underlying causal relationships, we can significantly boost the interpretability and identifiability of label enhancement. Meanwhile, except for the relevance consistency, LESCM are encouraged to sustain the order consistency that assigns higher description degree of the recovered label distribution to the positive labels, as compared with the negative labels. Empirically, sufficient experiments on several label distribution learning data sets validate the effectiveness of LESCM. © 1989-2012 IEEE.
Keyword ：
Label Distribution Learning Label Distribution Learning Label Enhancement Label Enhancement Learning with Ambiguity Learning with Ambiguity Structured Causal Model Structured Causal Model
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| GB/T 7714 | Liu, X. , Zhu, J. , Yue, K. et al. Causal Label Enhancement [J]. | IEEE Transactions on Knowledge and Data Engineering , 2024 . |
| MLA | Liu, X. et al. "Causal Label Enhancement" . | IEEE Transactions on Knowledge and Data Engineering (2024) . |
| APA | Liu, X. , Zhu, J. , Yue, K. , Zheng, Q. , Li, Z. , Tian, Z. et al. Causal Label Enhancement . | IEEE Transactions on Knowledge and Data Engineering , 2024 . |
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With the extensive use of multi-view data in practice, multi-view spectral clustering has received a lot of attention. In this work, we focus on the following two challenges, namely, how to deal with the partially contradictory graph information among different views and how to conduct clustering without the parameter selection. To this end, we establish a novel graph learning framework, which avoids the linear combination of the partially contradictory graph information among different views and learns a unified graph for clustering without the parameter selection. Specifically, we introduce a flexible graph degeneration with a structured graph constraint to address the aforementioned challenging issues. Besides, our method can be employed to deal with large-scale data by using the bipartite graph. Experimental results show the effectiveness and competitiveness of our method, compared to several state-of-the-art methods. IEEE
Keyword ：
Bipartite graph Bipartite graph Circuits and systems Circuits and systems graph degeneration graph degeneration Laplace equations Laplace equations Multi-view data Multi-view data Optimization Optimization structured graph constraint structured graph constraint Task analysis Task analysis Time complexity Time complexity Vectors Vectors
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| GB/T 7714 | Zheng, Q. . Flexible and Parameter-free Graph Learning for Multi-view Spectral Clustering [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 (9) : 1-1 . |
| MLA | Zheng, Q. . "Flexible and Parameter-free Graph Learning for Multi-view Spectral Clustering" . | IEEE Transactions on Circuits and Systems for Video Technology 34 . 9 (2024) : 1-1 . |
| APA | Zheng, Q. . Flexible and Parameter-free Graph Learning for Multi-view Spectral Clustering . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 (9) , 1-1 . |
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Multi-view clustering aims to improve the clustering performance by leveraging information from multiple views. Most existing works assume that all views are complete. However, samples in real-world scenarios cannot be always observed in all views, leading to the challenging problem of Incomplete Multi-View Clustering (IMVC). Although some attempts are made recently, they still suffer from the following two limitations: (1) they usually adopt shallow models, which are unable to sufficiently explore the consistency and complementary of multiple views; (2) they lack of a suitable measurement to evaluate the quality of the recovered data during the learning process. To address the aforementioned limitations, we introduce a novel Incomplete Multi-View Clustering via Inference and Evaluation (IMVC-IE). Specifically, IMVC-IE adopts the contrastive learning strategy on features of different views to excavate the underlying information from existing samples firstly. Subsequently, massive alternative simulated data are inferred for missing views and a novel evaluation strategy is presented to obtain the proper data for missing views completion. Extensive experiments are conducted and verify the effectiveness of our method. © 2024 IEEE.
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| GB/T 7714 | Huang, Binqiang , Huang, Zhijie , Lan, Shoujie et al. INCOMPLETE MULTI-VIEW CLUSTERING VIA INFERENCE AND EVALUATION [C] . 2024 : 8180-8184 . |
| MLA | Huang, Binqiang et al. "INCOMPLETE MULTI-VIEW CLUSTERING VIA INFERENCE AND EVALUATION" . (2024) : 8180-8184 . |
| APA | Huang, Binqiang , Huang, Zhijie , Lan, Shoujie , Zheng, Qinghai , Yu, Yuanlong . INCOMPLETE MULTI-VIEW CLUSTERING VIA INFERENCE AND EVALUATION . (2024) : 8180-8184 . |
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Multi -view clustering leverages diverse information sources for unsupervised clustering. While existing methods primarily focus on learning a fused representation matrix, they often overlook the impact of private information and noise. To overcome this limitation, we propose a novel approach, the Multi -view Semantic Consistency based Information Bottleneck for Clustering (MSCIB). Our method emphasizes semantic consistency to enhance the information bottleneck learning process across different views. It aligns multiple views in the semantic space, capturing valuable consistent information from multi -view data. The learned semantic consistency improves the ability of the information bottleneck to precisely distinguish consistent information, resulting in a more discriminative and unified feature representation for clustering. Experimental results on diverse multi -view datasets demonstrate that MSCIB achieves state-of-the-art performance. In comparison with the average performance of the other contrast algorithms, our approach exhibits a notable improvement of at least 4%.
Keyword ：
Contrastive clustering Contrastive clustering Information bottleneck Information bottleneck Multi-view clustering Multi-view clustering
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| GB/T 7714 | Yan, Wenbiao , Zhou, Yiyang , Wang, Yifei et al. Multi-view Semantic Consistency based Information Bottleneck for Clustering [J]. | KNOWLEDGE-BASED SYSTEMS , 2024 , 288 . |
| MLA | Yan, Wenbiao et al. "Multi-view Semantic Consistency based Information Bottleneck for Clustering" . | KNOWLEDGE-BASED SYSTEMS 288 (2024) . |
| APA | Yan, Wenbiao , Zhou, Yiyang , Wang, Yifei , Zheng, Qinghai , Zhu, Jihua . Multi-view Semantic Consistency based Information Bottleneck for Clustering . | KNOWLEDGE-BASED SYSTEMS , 2024 , 288 . |
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Federated learning encounters substantial challenges with heterogeneous data, leading to performance degradation and convergence issues. While considerable progress has been achieved in mitigating such an impact, the reliability aspect of federated models has been largely disregarded. In this study, we conduct extensive experiments to investigate the reliability of both generic and personalized federated models. Our exploration uncovers a significant finding: federated models exhibit unreliability when faced with heterogeneous data, demonstrating poor calibration on in-distribution test data and low uncertainty levels on out-of-distribution data. This unreliability is primarily attributed to the presence of biased projection heads, which introduce miscalibration into the federated models. Inspired by this observation, we propose the'Assembled Projection Heads' (APH) method for enhancing the reliability of federated models. By treating the existing projection head parameters as priors, APH randomly samples multiple initialized parameters of projection heads from the prior and further performs targeted fine-tuning on locally available data under varying learning rates. Such a head ensemble introduces parameter diversity into the deterministic model, eliminating the bias and producing reliable predictions via head averaging. We evaluate the effectiveness of the proposed APH method across three prominent federated benchmarks. Experimental results validate the efficacy of APH in model calibration and uncertainty estimation. Notably, APH can be seamlessly integrated into various federated approaches but only requires less than 30% additional computation cost for 100× inferences within large models. Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Artificial intelligence Artificial intelligence Reliability Reliability
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| GB/T 7714 | Chen, Jinqian , Zhu, Jihua , Zheng, Qinghai et al. Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models [C] . 2024 : 11329-11337 . |
| MLA | Chen, Jinqian et al. "Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models" . (2024) : 11329-11337 . |
| APA | Chen, Jinqian , Zhu, Jihua , Zheng, Qinghai , Li, Zhongyu , Tian, Zhiqiang . Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models . (2024) : 11329-11337 . |
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