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incomplete multi-view clustering With Paired and Balanced Dynamic Anchor Learning

IEEE TRANSACTIONS ON multiMEDIA 2025年 27卷 1486-1497页

作者： Li, Xingfeng Pan, Yuangang Sun, Yuan Sun, Quansen Sun, Yinghui Tsang, Ivor W. Ren, Zhenwen Southwest Univ Sci & Technol Dept Natl Def Sci & Technol Mianyang 621010 Peoples R China Nanjing Univ Sci & Technol Dept Comp Sci Nanjing 210094 Peoples R China ASTAR Ctr Frontier AI Res Singapore 138632 Singapore Sichuan Univ Coll Comp Sci Chengdu 610044 Peoples R China Southwest Univ Sci & Technol Dept Natl Def Sci & Technol Mianyang 621053 Peoples R China Guangxi Key Lab Digital Infrastruct Nanning 530000 Peoples R China

Compared to static anchor selection, existing dynamic anchor learning could automatically learn more flexible anchors to improve the performance of large-scale multi-view clustering. Despite improving the flexibility of anchors, these methods do not pay sufficient attention to the alignment and fairness of learned anchors. Specifically, within each cluster, the positions and quantities of cross-view anchors may not align, or even anchor absence in some clusters, leading to severe anchor misalignment and imbalance issues. These issues result in inaccurate graph fusion and a reduction in clustering performance. Besides, in practical applications, missing information caused by sensor malfunctions or data losses could further exacerbate anchor misalignment and imbalance. To overcome such challenges, a novel incomplete multi-view clustering with Paired and Balanced Dynamic Anchor Learning (PBDAL) is proposed to ensure the alignment and fairness of anchors. Unlike existing unsupervised anchor learning, we first design a paired and balanced dynamic anchor learning scheme to supervise dynamic anchors to be aligned and fair in each cluster. Meanwhile, we develop an enhanced bipartite graph tensor learning to refine paired and balanced anchors. Our superiority, effectiveness, and efficiency are all validated by performing extensive experiments on multiple public datasets.

关键词： Sun Tensors Vectors Optimization Computer science Complexity theory Bipartite graph Artificial intelligence Feature extraction Surges Dynamic anchor learning incomplete multi-view clustering bipartite graph learning pseudo-label supervise learning low-rank tensor

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incomplete multi-view clustering based on information fusion with self-supervised learning

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INFORMATION FUSION 2025年 117卷

作者： Cai, Yilong Shu, Qianyu Zhou, Zhengchun Meng, Hua Southwest Jiaotong Univ Sch Math Chengdu 611756 Peoples R China Sichuan Normal Univ Sch Math Sci Chengdu 610066 Peoples R China

clustering algorithms aim to analyze data structures and properties, grouping the data based on their underlying structural characteristics. Traditional multi-view clustering algorithms focus on combining data shared by multiple views to perform cluster analysis, and such algorithms typically limit the completeness of the data in each view. In real-world applications, it is common that the samples within each view are missing, which reduces clustering performance. In this paper, we propose a novel incomplete multi-view clustering algorithm that addresses the sample-missing problem by leveraging self-supervised information fusion to integrate both global and local information. Data pair construction, global information extraction, and missing information completion are three core modules for the proposed algorithm: (1) constructing multi- view positive and negative pairs through contrastive learning to identify the differences between data samples in a self-supervised manner;(2) forming adjacency matrices to capture the manifold structure of the entire sample data and introducing spectral loss to tighten the representation of similar clusters in the feature layer;and (3) employing the kernel regression method to estimate the missing information in a nonlinear manner by calculating the cross-view metric relationship between the existing data and the missing data, thereby improving data integrity. The proposed network framework combines local information complementation and global information extraction. The experimental results show that our proposed method outperforms existing state-of-the-art methods, achieving an average performance gain of 4.75% accuracy across multiple datasets.

关键词： incomplete multi-view clustering Sample-missing problem Information fusion Contrastive learning

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Strengthening incomplete multi-view clustering: An attention contrastive learning method

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IMAGE AND VISION COMPUTING 2025年 157卷

作者： Hou, Shudong Guo, Lanlan Wei, Xu Anhui Univ Technol Sch Comp Sci & Technol Maanshan 243002 Anhui Peoples R China

incomplete multi-view clustering presents greater challenges than traditional multi-view clustering. In recent years, significant progress has been made in this field, multi-view clustering relies on the consistency and integrity of views to ensure the accurate transmission of data information. However, during the process of data collection and transmission, data loss is inevitable, leading to partial view loss and increasing the difficulty of joint learning on incomplete multi-view data. To address this issue, we propose a multi-view contrastive learning framework based on the attention mechanism. Previous contrastive learning mainly focused on the relationships between isolated sample pairs, which limited the robustness of the method. Our method selects positive samples from both global and local perspectives by utilizing the nearest neighbor graph to maximize the correlation between local features and latent features of each view. Additionally, we use a cross-view encoder network with self-attention structure to fuse the low dimensional representations of each view into a joint representation, and guide the learning of the joint representation through a high confidence structure. Furthermore, we introduce graph constraint learning to explore potential neighbor relationships among instances to facilitate data reconstruction. The experimental results on six multi-view datasets demonstrate that our method exhibits significant effectiveness and superiority compared to existing methods.

关键词： incomplete multi-view clustering Cross-view encoder Contrastive learning High confidence Graph constraint

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Neighbor structure aware based cross-view consensus graph learning for incomplete multi-view clustering

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NEUROCOMPUTING 2025年 624卷

作者： Yang, Haifeng Li, Xingyi Chen, Yijing Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Peoples R China Jiangsu Univ Sch Comp Sci Zhenjiang Peoples R China Jiangsu Univ Sch Comp Sci & Commun Engn Comp Technol Zhenjiang Peoples R China

multi-view clustering has gained significant attention due to its ability to fully leverage the relationships between different views. In the face of the incomplete observability of complex systems, incomplete multi-view clustering (IMVC) has become an important research direction in machine learning. Currently, graph-based methods have shown excellent performance in clustering research. However, there are still some shortcomings: first, some methods only focus on non-missing sample information;second, most only explore pairwise relationships between samples while neglecting connections between neighboring clusters;finally, many methods fail to simultaneously utilize global structural information and potential information between views. To address these issues, this paper proposes a neighbor-group structure-aware cross-view incomplete multi-view clustering method (NACG_IMVC), aimed at exploring the relationships between sample neighbor groups. This method constructs a high-quality consensus graph by leveraging intra-view local information and inter-view global structural information. Additionally, a novel indicator matrix is designed to account for the structure of missing data, thereby enhancing the quality of the consensus graph. Experimental results on multiple datasets indicate that the clustering performance of NACG_IMVC surpasses that of several advanced clustering methods.

关键词： Graph learning incomplete multi-view clustering Cross-view representation Structure preservation

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Robust high-order graph learning for incomplete multi-view clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Wang, Daoyuan Ren, Fujian Zhuang, Yuntang Liang, Cheng Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China

incomplete multi-view clustering is a challenging problem in unsupervised learning that involves grouping multi-view data with missing information into distinct clusters. In this study, we propose a novel robust high-order graph learning (RHGL) method for incomplete multi-view clustering. Specifically, a robust principal component analysis is first employed to gain error-free matrices based on the original available data and then the obtained robust representations are utilized for local manifold learning. Next, we align the view-specific partitions generated from the local similarity structures and perform complete similarity graph learning to explore the global similarity structure. To capture the high-order relationships among views, all complete similarity graphs are stacked into a third-order tensor with the low-rank constraint. As a result, our model can simultaneously explore both local and global similarity structures and mine high-order inter-view correlations. An effective iterative optimization strategy is developed to solve the proposed approach. Experiments on six benchmark datasets show that our model outperforms the competing methods.

关键词： incomplete multi-view clustering Graph learning Partition learning Low-rank tensor constraint

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Prototype Matching Learning for incomplete multi-view clustering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2025年 34卷 828-841页

作者： Yuan, Honglin Sun, Yuan Zhou, Fei Wen, Jing Yuan, Shihua You, Xiaojian Ren, Zhenwen Southwest Univ Sci & Technol Sch Natl Def Sci & Technol Mianyang 621010 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610044 Peoples R China Guangxi Key Lab Digital Infrastruct Nanning 530201 Peoples R China

As information acquisition diversifies, data is acquired and stored in increasing modalities. However, sensor failures or equipment issues can lead to partial data loss in certain views, resulting in incomplete multi-view clustering (IMVC) problems. Although some prototype-based IMVC methods have achieved satisfactory performance, almost all of these methods implicitly assume that the cross-view prototypes are aligned. However, during the generation or selection of prototypes, different networks could produce different prototypes, thereby leading to potential misalignment of prototypes across views, i.e., prototype-unaligned problem (PUP). The presence of PUP could lead to overfitting the model. Additionally, when recovering the missing data, there is uncertainty in data quality under different missing rates, which could lead to the performance instability problem (PIP). To address these issues, we propose Prototype Matching Learning for incomplete multi-view clustering (PMIMC). Specifically, PMIMC leverages relational consistency learning to mitigate the heterogeneity of multi-view data. Subsequently, we design a robust prototype contrastive learning loss for the generated prototypes to reduce the effects of PUP. Finally, we propose a prototype-based imputation strategy, that aims to alleviate the instability of imputation under high missing rates. Extensive experiments demonstrate that PMIMC outperforms 13 state-of-the-art methods in terms of clustering performance and robustness. The code is available at: https://***/hl-yuan/PMIMC.

关键词： Prototypes Overfitting Robustness Imputation Sun Noise Decoding Correlation Uncertainty incomplete multi-view clustering multi-view representation learning prototype-unaligned problem contrastive learning prototype matching prototype matching

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Consensus Partition Guided incomplete multi-view clustering

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IEEE ACCESS 2025年 13卷 40198-40209页

作者： Yang, Chunyu Yue, Hongyun Xian Univ Architecture & Technol Coll Sci Xian 710064 Shaanxi Peoples R China

incomplete multi-view clustering aims to derive a comprehensive consensus partition that is shared across all views, a topic that has attracted significant attention in recent years. Most existing methods for incomplete multi-view clustering attempt to directly learn the consensus partition matrix from noisy, complete or incomplete graphs. However, these approaches have limitations: they often overlook the discriminative information within view-specific partition matrices and fail to fully consider the relationships between the partition and the graph. To address these issues, we introduce two new regularization strategies in this paper. One regularity employs a consensus partition matrix to guide the learning process for multiple complete graphs. The other regularity combines multiple view-specific partition matrices to ensure consistency across different views at the discriminative partition level. Building on these concepts, we propose a novel approach for incomplete multi-view clustering, called Consensus Partition-guided incomplete multi-view clustering (CPIMC). This method leverages consensus information at the partition level to complete multiple incomplete graphs and enforce consistency across views. Extensive experiments conducted on several commonly used incomplete multi-view datasets show that CPIMC outperforms state-of-the-art methods in incomplete multi-view clustering.

关键词： Manifolds Matrix decomposition Tensors Laplace equations Solid modeling Optimization Indexes Hands Coherence Stacking incomplete multi-view clustering consensus partition complete graphs learning discriminative information

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Robust Mixed-order Graph Learning for incomplete multi-view clustering

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INFORMATION FUSION 2025年 115卷

作者： Guo, Wei Che, Hangjun Leung, Man-Fai Jin, Long Wen, Shiping Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Anglia Ruskin Univ Fac Sci & Engn Cambridge CB1 1PT England Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

incomplete multi-view clustering (IMVC) aims to address the clustering problem of multi-view data with partially missing samples and has received widespread attention in recent years. Most existing IMVC methods still have the following issues that require to be further addressed. They focus solely on the first-order correlation information among samples, neglecting the more intricate high-order connections. Additionally, these methods always overlook the noise or inaccuracies in the self-representation matrix. To address above issues, a novel method named Robust Mixed-order Graph Learning (RMoGL) is proposed for IMVC. Specifically, to enhance the robustness to noise, the self-representation matrices are separated into clean graphs and noise graphs. To capture complex high-order relationships among samples, the dynamic high-order similarity graphs are innovatively constructed from the recovered data. The clean graphs are endowed with mixed-order information and tend towards to obtain a consensus graph via a self-weighted manner. An efficient algorithm based on Alternating Direction Method of multipliers (ADMM) is designed to solve the proposed RMoGL, and superior performance is demonstrated by compared with nine state-of-the-art methods across eight datasets. The source code of this work is available at https://***/guowei1314/RMoGL.

关键词： incomplete multi-view clustering High-order relationships Similarity matrix Consensus graph Self-weighted manner

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Tensorized graph-guided view recovery for incomplete multi-view clustering

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APPLIED INTELLIGENCE 2025年第7期55卷 1-16页

作者： Zheng, Li Yan, Guanghui Tang, Chunyang Yan, Tianfeng Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou Peoples R China

multi-view clustering (MVC) methods have demonstrated remarkable success when all samples are available across multiple views by leveraging consistency and complementary information. However, real-world multi-view data often suffers from incompleteness, where some samples are missing in one or more views. This incompleteness makes MVC challenging, as it becomes difficult to uncover consistency and complementary relationships among the view data. As a result, incomplete multi-view clustering (IMVC) has emerged to address the limitations posed by missing data. An intuitive approach to tackle this issue is view recovery-effectively leveraging consistency information from multiple views to impute missing data. However, the quality of view recovery heavily depends on the learned consistency information, making it crucial to learn high-quality consistency representations. To address this challenge, we propose a novel approach called Tensorized Graph-Guided view Recovery (TGGVR), which integrates view recovery and tensorized graph learning within a unified framework. The tensorized graph learning estimate a similarity graph for each view by exploiting consistency and complementary information through tensorized learning. In addition, high-quality neighborhood structures are exploited to obtain a more accurate consensus graph. This high-quality consensus graph then guides the more accurate recovery of missing data, establishing a cyclical procedure in which tensorized graph learning and data imputation mutually reinforce each other. Experimental results demonstrate that our proposed method outperforms several state-of-the-art approaches in tackling the challenging task of IMVC. Notably, our method significantly outperforms representative competing methods by more than 5% and 10% on the BBC and Caltech datasets, respectively.

关键词： Robust tensorized graph learning incomplete multi-view clustering

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An incomplete multi-view clustering approach using subspace alignment constraint

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年第4期16卷 2487-2502页

作者： Niu, Xueying Zhao, Xiaojie Hu, Lihua Zhang, Jifu Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Waliu Rd 66 Taiyuan 030024 Peoples R China

For partially missing multi-view data, existing incomplete multi-view clustering approaches suffer from the effects of misplaced neighbour relationships and changes in subspace structure caused by data incompleteness and imbalances in clustering information among views. In this paper, an incomplete multi-view clustering approach is proposed using subspace alignment constraints. Firstly, subspace alignment constraint is proposed among incomplete subspace representations to mitigate the misalignment-related changes in clustering structures. An adaptive weight is proposed to balance clustering information among views. Secondly, an incomplete multi-view clustering model is proposed by integrating subspace alignment constraint and inter-view adaptive representation, and a clustering algorithm is designed using an alternate minimizing optimization strategy. In the end, experimental results on benchmark datasets validate the algorithm's excellent clustering performance for incomplete multi-view datasets.

关键词： incomplete multi-view clustering Subspace alignment constraint Relationship misalignment Adaptive weight

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