Highly-efficient Incomplete Largescale Multiview Clustering with Consensus Bipartite Graph

Wang, Siwei; Liu, Xinwang; Liu, Li; Tu, Wenxuan; Zhu, Xinzhong; Liu, Jiyuan; Zhou, Sihang; Zhu, En

doi:10.1109/cvpr52688.2022.00955

Cited by 73 publications

(22 citation statements)

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“…Along with our proposed EMVGC-LG, we run ten state-of-the-art multi-view graph clustering methods for comparison, including Multi-view k-means Clustering on Big Data (RMKM) [1], Parameterfree Auto-weighted Multiple Graph Learning (AMGL) [40], Flexible Multi-View Representation Learning for Subspace Clustering (FMR) [23], Partition Level Multi-view Subspace Clustering (PMSC) [18], Binary Multi-View Clustering (BMVC) [79], Large-scale Multiview Subspace Clustering in Linear Time (LMVSC) [19], Scalable Multiview Subspace Clustering with Unified Anchors (SMVSC) [49], Multi-view clustering: a Scalable and Parameter-free Bipartite Graph Fusion Method (SFMC) [24], Fast Multiview Clustering via Nonnegative and Orthogonal Factorization (FMCNOF) [68], and Fast Parameter-free Multiview Subspace Clustering with Consensus Anchor Guidance (FPMVS-CAG) [55].…”

Section: Compared Methodsmentioning

confidence: 99%

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

Wen,

Liu,

Wan

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views. Intuitively, a high-quality anchor graph plays an essential role in the success of AMVGC. However, the existing AMVGC methods only consider single-structure information, i.e., local or global structure, which provides insufficient information for the learning task. To be specific, the over-scattered global structure leads to learned anchors failing to depict the cluster partition well. In contrast, the local structure with an improper similarity measure results in potentially inaccurate anchor assignment, ultimately leading to sub-optimal clustering performance. To tackle the issue, we propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified framework with a theoretical guarantee is designed to capture local and global information. Besides, EMVGC-LG jointly optimizes anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number, which is time-economical and scales well with the data size. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.

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Section: Compared Methodsmentioning

confidence: 99%

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

Wen,

Liu,

Wan

et al. 2023

Proceedings of the 31st ACM International Conference on Multimedia

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show abstract

“…Clustering Metrics We adopt four widely-used clustering metrics to evaluate all compared methods, i.e., Accuracy (ACC), Normalized Mutual Information (NMI), Average Rand Index (ARI), and macro F1-score (F1) (Wang et al 2022a;Wan et al 2022Wan et al , 2023Li et al 2022aLi et al , 2023b.…”

Section: Computational Complexitymentioning

confidence: 99%

Attribute-Missing Graph Clustering Network

Tu,

Guan,

Zhou

et al. 2024

AAAI

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Deep clustering with attribute-missing graphs, where only a subset of nodes possesses complete attributes while those of others are missing, is an important yet challenging topic in various practical applications. It has become a prevalent learning paradigm in existing studies to perform data imputation first and subsequently conduct clustering using the imputed information. However, these ``two-stage" methods disconnect the clustering and imputation processes, preventing the model from effectively learning clustering-friendly graph embedding. Furthermore, they are not tailored for clustering tasks, leading to inferior clustering results. To solve these issues, we propose a novel Attribute-Missing Graph Clustering (AMGC) method to alternately promote clustering and imputation in a unified framework, where we iteratively produce the clustering-enhanced nearest neighbor information to conduct the data imputation process and utilize the imputed information to implicitly refine the clustering distribution through model optimization. Specifically, in the imputation step, we take the learned clustering information as imputation prompts to help each attribute-missing sample gather highly correlated features within its clusters for data completion, such that the intra-class compactness can be improved. Moreover, to support reliable clustering, we maximize inter-class separability by conducting cost-efficient dual non-contrastive learning over the imputed latent features, which in turn promotes greater graph encoding capability for clustering sub-network. Extensive experiments on five datasets have verified the superiority of AMGC against competitors.

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“…Based on this, Liu et al (2022b) and Chen et al (2022b) respectively added rank constraints and orthogonal decomposition terms as constraints to learn the clustering results directly. Li et al (2022), Wang et al (2022) and Liu et al (2022a) applied it to clustering incomplete multi-view datasets. Li et al (2023) considered that noisy features in multi-view data lead to anchor shift during optimization.…”

Section: Algorithms Based On Matrix Factorizationmentioning

confidence: 99%

The methods for improving large-scale multi-view clustering efficiency: a survey

Yang,

Tan

2024

Artif Intell Rev

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The diversity and large scale of multi-view data have brought more significant challenges to conventional clustering technology. Recently, multi-view clustering has received widespread attention because it can better use different views’ consensus and complementary information to improve clustering performance. Simultaneously, many researchers have proposed various algorithms to reduce the computational complexity to accommodate the demands of large-scale multi-view clustering. However, the current reviews do not summarize from the perspective of reducing the computational complexity of large-scale multi-view clustering. Therefore, this paper outlines various high-frequency methods used in recent years to reduce the computational complexity of large-scale multi-view clustering, i.e. third-order tensor t-SVD, anchors-based graph construction, matrix blocking, and matrix factorization, and compares the corresponding algorithms based on several open datasets. Finally, the strengths and weaknesses of the current algorithm and the point of improvement are analyzed.

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Highly-efficient Incomplete Largescale Multiview Clustering with Consensus Bipartite Graph

Cited by 73 publications

References 0 publications

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

Attribute-Missing Graph Clustering Network

The methods for improving large-scale multi-view clustering efficiency: a survey

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