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Deep spectral clustering network for incomplete multi-view clustering

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 148卷

作者： Li, Ao Mei, Sanlin Feng, Cong Gao, Tianyu Huang, Hai Harbin Univ Sci & Technol Sch Comp Sci & Technol 52 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China

Spectral clustering is a helpful technique for clustering non-convex data, which extends the clustering to data with multiple partial views. Still, it has a higher running time thanks to cubic time complexity, while extending spectral embeddings to unseen samples is non-trivial and prevents model reuse. In light of this, we propose a Deep Spectral clustering Network for incomplete multi-view clustering (DSCN-IMC). At its core, DSCNIMC jointly learns the approximate spectral embeddings and the soft cluster assignments using a deep neural network in an unsupervised and end-to-end fashion. Specifically, we integrate the graph convolutional layers and manifold loss into our network to achieve robustness against incomplete multi-view data. After that, an orthnormalization layer is exploited to fulfill the orthogonal constraint of spectral embeddings, thereby achieving the lower running time of spectral clustering. Building upon this, a self-supervised clustering module is introduced to obtain cluster assignments of unseen samples and facilitates model reuse. Our method's efficacy is demonstrated through experiments conducted on nine datasets, wherein we compare its performance against nine state-of-the-art baselines.

关键词： Graph convolutional networks incomplete multi-view clustering Representation learning Spectral clustering

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Dual Completion Learning for incomplete multi-view clustering

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 455-467页

作者： Shen, Qiangqiang Zhang, Xuanqi Wang, Shuqin Li, Yuanman Liang, Yongsheng Chen, Yongyong Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Guangdong Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Shandong Univ Aeronaut Coll Sci Binzhou 256600 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China

incomplete multi-view clustering (IMVC) offers a way to analyze incomplete data, facilitating the inference of unobserved and missing data points through completion techniques. However, existing IMVC methods, predominantly depending on either data completion or similarity matrix completion, failed to uncover the inherent geometric structure and potential complementary information between intra- and inter-views, causing incomplete similarity matrices to further tear apart the connections between views. To address this problem, we propose Dual Completion Learning for incomplete multi-view clustering (DCIMC), which elaborately designs data completion and similarity tensor completion, and fuses both of them into a unified model to effectively recover the missing samples and similarities. Concretely, in data completion, DCIMC utilizes subspace clustering to recover the missing and unknown instances directly. Meanwhile, in similarity tensor completion, DCIMC introduces the idea of tensor completion to make better use of the high-order complementary information from multi-view data. By fusing the dual completions, missing information and complementary information in each completion are fully explored by each other, reciprocally enhancing one another to boost the accuracy of our clustering algorithm. Experimental results on various datasets show the effectiveness of the proposed DCIMC. Moreover, our DCIMC also achieved superior or comparable performance in an extended comparison with recent deep learning-based multi-view clustering algorithms.

关键词： Tensors clustering algorithms Laplace equations Matrix decomposition Learning systems Matrix converters Kernel incomplete multi-view clustering subspace clustering data completion tensor completion

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Adaptive structural-guided multi-level representation learning with graph contrastive for incomplete multi-view clustering

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INFORMATION FUSION 2025年 119卷

作者： Wang, Haiyue Zhang, Wensheng Wang, Quan Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xidian Univ Key Lab Smart Human Comp Interact & Wearable Techn 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Guangdong Peoples R China

incomplete multi-view clustering (IMC) is a pivotal task within the area of machine learning, encompassing several unresolved challenges, such as representation of objects, relations of various views, discriminative of features, and data restoration. To address these challenges, we propose a novel Adaptive Structural-guided multi-level representation Learning with Graph Contrastive algorithm for IMC (ASMLGC), where feature learning, data restoration and clustering are simultaneously integrated. Concretely, ASMLGC learns multilevel representations of objects by extending auto-encoders, which explicitly captures hierarchical structure of multi-view data, providing abetter and more comprehensive strategy to characterize data from multiple resolutions. And, missing views are recovered by leveraging multi-level representations, where global and local information are fully exploited to enhance the accuracy and robustness of imputation. Furthermore, ASMLGC proposes graph contrastive learning to maximize intra-cluster consistency, where information derived from various resolutions, such as feature level and meta-structure level, is explored to construct positive and negative samples, thereby improving discriminative of features. The extensive experimental results confirm that ASMLGC outperforms baselines on benchmarking datasets, particularly for these datasets with complicated hierarchical structure. The proposed algorithm can be applied to bioinformatics, medical image analysis, and social network analysis.

关键词： incomplete multi-view clustering Structural multi-level representation Graph contrastive learning

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Dual structure-aware consensus graph learning for incomplete multi-view clustering

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PATTERN RECOGNITION 2025年 165卷

作者： Sun, Lilei Wong, Wai Keung Fu, Yusen Wen, Jie Li, Mu Lu, Yuwu Fei, Lunke Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang Peoples R China Hong Kong Polytech Univ Sch Fash & Text Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Peoples R China South China Normal Univ Sch Software Foshan Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China

Compared to single-view data, multi-view data encompasses both additional complementary information and redundancies. The discriminative information presented in these aligned multiple views is helpful for enhancing the performance of clustering tasks. In reality, data views are frequently incomplete, which poses a significant challenge to the clustering task. In this paper, we introduce a new method, which we called Structured-aware Consensus Graph Learning for incomplete multi-view clustering (SWCGLIMVC) to tackle the problem of incomplete multi-view clustering (IMVC). Specifically, considering that the neighbor relationships between samples are of utmost importance in unsupervised clustering tasks, SWCGLIMVC leverages the intrinsic geometry structure information of all samples and preserves their neighbor relationships through the graph Laplacian regularization constraint. Moreover, to reduce the adverse effects of the imbalanced useful information contained in different views, SWCGLIMVC incorporates a dynamically learnable vector to constrain the learning models of different views. This allows the method to effectively explore the information from all incomplete views for data clustering tasks. The effectiveness of SWCGLIMVC is evaluated by conducting experiments on six widely known datasets with the comparison of several state-of-the-art clustering methods. The experimental results show that the superior performance of SWCGLIMVC on IMVC tasks.

关键词： Graph learning incomplete multi-view clustering Missing views

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incomplete multi-view clustering based on hypergraph

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INFORMATION FUSION 2025年 117卷

作者： Chen, Jin Xu, Huafu Xue, Jingjing Gao, Quanxue Deng, Cheng Lv, Ziyu Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Guangxi Informat Ctr Guangxi Key Lab Digital Infrastructure Nanning 530000 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China BYD Automot Ind Co Ltd Automot Engn Res Inst Xian 710119 Peoples R China

The graph-based incomplete multi-view clustering aims at integrating information from multiple views and utilizes graph models to capture the global and local structure of the data for reconstructing missing data, which is suitable for processing complex data. However, ordinary graph learning methods usually only consider pairwise relationships between data points and cannot unearth higher-order relationships latent in the data. And existing graph clustering methods often divide the process of learning the representations and the clustering process into two separate steps, which may lead to unsatisfactory clustering results. Besides, they also tend to consider only intra-view similarity structures and overlook inter-view ones. To this end, this paper introduces an innovative one-step incomplete multi-view clustering based on hypergraph (IMVC_HG). Specifically, we use a hypergraph to reconstruct missing views, which can better explore the local structure and higher-order information between sample points. Moreover, we use non-negative matrix factorization with orthogonality constraints to equate K-means, which eliminates post-processing operations and avoids the problem of suboptimal results caused by the two-step approach. In addition, the tensor Schatten p-norm is used to better capture the complementary information and low-rank structure between the cluster label matrices of multiple views. Numerous experiments verify the superiority of IMVC_HG.

关键词： incomplete multi-view clustering Graph learning Hypergraph Non-negative matrix factorization

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Deep incomplete multi-view clustering via Dynamic Imputation and Triple Alignment With Dual Optimization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第4期35卷 3250-3261页

作者： Yan, Weiqing Liu, Kanglong Zhou, Wujie Tang, Chang Yantai Univ Sch Comp & Control Engn Yantai 261400 Peoples R China Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Zhejiang Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

In recent years, incomplete multi-view clustering (IMVC) has become an important and challenging task. Although several methods have been proposed to address IMVC, they still have the following drawbacks: i) Due to the presence of missing samples in the views, clustering prototypes obtained from different views may have positional deviations, leading to inaccurate positioning of cluster centers, thus affecting the accuracy of clustering results. ii) Repair strategies based on cross-view prediction and adversarial generation have high computational costs and heavily rely on model performance. Neighbor-based repair strategies may result in inaccurate neighbor selection due to the presence of noise. iii) Models learned solely from complete data often perform better than models learned from both complete and incomplete data, especially when there are semantic differences between the repaired data and the missing data. To address the aforementioned issues, this paper proposes a Dynamic Imputation and Triple Alignment with Dual-Optimization for Deep incomplete multi-view clustering (DITA-IMVC). Specifically, by accurately representing advanced features, we propose a cross-view dynamic structure learning strategy for missing view repair, where the dynamic structural relationships between high-semantic features within each view are calculated to obtain highly related samples from different views. To address positional deviations from different views, we propose a triple cross-view alignment with prototype, feature, and clustering assignment, which preserves the consistency among different views. Finally, we design a dual-optimization process for both complete view features and repaired features via alternating iterations to fully utilize the incomplete view data, thereby improving clustering performance. To demonstrate the effectiveness of our DITA-IMVC, extensive experiments conducted on different standard datasets show that it yields superior clustering results compared t

关键词： Data models Prototypes Maintenance engineering Computational modeling Vehicle dynamics Predictive models Optimization Feature extraction Semantics Noise incomplete multi-view clustering prototype alignment feature alignment

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Anchor-based incomplete multi-view clustering with graph convolution network

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APPLIED INTELLIGENCE 2025年第7期55卷 1-15页

作者： Li, Ao Gao, Tianyu Wang, Yanbing Feng, Cong Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Harbin Univ Sci & Technol Sch Measure Control Technol & Commun Engn Harbin 150001 Peoples R China

Anchor-based method has proved to be effective in recent incomplete multi-view clustering literature. Although existing methods have achieved significant success in various fields (e.g., digital treatment), they still have several limitations: (1) The construction of anchor graph insufficiently considers the graph structural information inherent in the original data. (2) Most studies are unable to sufficiently explore the correlation between the non-linear structures of representation space and the original space. In this paper, we propose a Anchor-based incomplete multi-view clustering with Graph Convolution Network (AIMCG) method to address the above issues. Specifically, we first adopt graph convolution networks to extract graph information from multi-view data, and employ manifold regularization to constrain the generation of common graph representation. Subsequently, we employ an anchor-based data reconstruction method to generate anchor g raph, combining previous graph information into this process to further enhance the clustering capability. Finally, spectral clustering is applied to the anchor graph to obtain the clustering results. Experiments on 9 benchmark datasets compared with 13 advanced baselines verify the effectiveness of our AIMCG method on incomplete multi-view data.

关键词： incomplete multi-view clustering Anchor-based clustering Consensus representation Graph convolutional network

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Prototype Imputation Guided incomplete multi-view clustering

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1565-1569页

作者： Hong, Rui Chen, Xiao-Ping Zhou, Yan Liu, Hui Wan, Tiancai Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Avic Chengdu Aircraft Design & Res Inst Chengdu 610091 Peoples R China

In real-world data collection, certain views may become partially unavailable due to sensor malfunctions or equipment faults, giving rise to the challenge of incomplete multi-view clustering (IMVC). Although some IMVC methods that utilize prototype completion yield acceptable performance, the majority implicitly assume the correct alignment of prototypes across all views. However, the generated prototypes could have the potential misalignment of prototypes across views. To tackle this issue, we propose a new Prototype Imputation Guided incomplete multi-view clustering (PIGIMC), which can reduce the impact of prototype mismatch. Specifically, RPCIC first employs a multi-view contrastive learning module to obtain consistent feature representations across various views. To mitigate the adverse impact caused by multi-view prototype mismatch, we propose a prototype matching learning to achieve prototype alignment. Lastly, we propose a prototype imputation strategy to impute the missing data, thereby achieving IMVC. Extensive experiments confirm that PIGIMC surpasses 11 state-of-the-art IMVC methods.

关键词： Prototypes Imputation Decoding Contrastive learning Training Data mining Machine learning Interference clustering algorithms Bipartite graph incomplete multi-view clustering prototype alignment prototype matching learning

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Balancing Complementarity and Consistency via Delayed Activation in incomplete multi-view clustering 7th

Balancing Complementarity and Consistency via Delayed Activa...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Li, Bo Xu, Zhiwei Yun, Jing Wang, Jiatai Inner Mongolia Univ Technol Coll Data Sci & Applicat Hohhot Peoples R China Haihe Lab ITAI Tianjin Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9789819787913;9789819787920

This paper study one challenging issue in incomplete multi-view clustering, where valuable complementary information from other views is always ignored. To be specific, we propose a framework that effectively balances Complementarity and Consistency information in incomplete multi-view clustering (CoCo-IMC). Specifically, we design a dual network of delayed activation, which achieves a balance of complementarity and consistency among different views. The delayed activation could enriches the complementarity information that was ignored during consistency learning. Then, we recover the incomplete information and enhance the consistency learning by minimizing the conditional entropy and maximizing the mutual information across different views. This could be the first theoretical attempt to incorporate delayed activation into incomplete data recovery and the balance of complementarity and consistency. We have proved the effectiveness of CoCo-IMC in extensive comparative experiments with 12 state-of-the-art baselines on four publicly available datasets.

关键词： incomplete multi-view clustering Contrastive Learning Complementarity Consistency Balance

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Adaptive graph learning for enhanced incomplete multi-view clustering

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PATTERN ANALYSIS AND APPLICATIONS 2025年第2期28卷 1-9页

作者： Hong, Rui Chen, Xiao-ping Zhou, Yan Liu, Hui Wan, Tiancai Bai, Taili Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Avic Chengdu Aircraft Design & Res Inst Chengdu 610091 Peoples R China

incomplete multi-view clustering (IMVC) aims to explore complementary and consistent information across incomplete views, yet existing IMVC methods overlook the potential hidden relationships between missing data. This paper proposes an adaptive graph learning approach for incomplete multi-view inferring and clustering (IMIC-AGL), which utilizes observable information between missing views to model and infer missing data. Specifically, missing data is modeled using robust principal component analysis, and adaptive neighborhood graph learning with rank constraints is employed to effectively fill in missing data. Experiments on benchmark datasets with varying missing rates demonstrate the effectiveness of IMIC-AGL, achieving state-of-the-art clustering performance, particularly under high missing ratios.

关键词： incomplete multi-view clustering Adaptive graph learning Robust principal component analysis

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