Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph

Li, Liang; Wang, Siwei; Liu, Xinwang; Zhu, En; Shen, Li; Li, Kenli; Li, Keqin

doi:10.1109/tnnls.2022.3184970

Cited by 25 publications

(5 citation statements)

References 59 publications

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“…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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Section: Computational Complexitymentioning

confidence: 99%

Attribute-Missing Graph Clustering Network

Tu,

Guan,

Zhou

et al. 2024

AAAI

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“…Fully paired data implies that all mapping relationships are given for every two cross-view data, as shown in Figure 2a. Multi-view clustering on fully paired data has been widely studied [32]- [35], which can be broadly classified into five categories. (1) Multiple kernel clustering [36]- [41].…”

Section: A Multi-view Clustering On Fully Paired Datamentioning

confidence: 99%

“…To the best of our knowledge, existing multi-view clustering algorithms can be roughly divided into three popular paradigms, including multi-view subspace clustering (MVSC) [15]- [24], multi-view graph clustering (MVGC) [25]- [30] and multiple kernel clustering (MKC) [31], [32]. Among them, MVSC first computes an independent subspace of each view and then fuses them into a unified subspace.…”

Section: Introductionmentioning

confidence: 99%

Continual Multi-view Clustering

Wan

Liu

Liang

et al. 2022

Proceedings of the 30th ACM International Conference on Multimedia

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Multi-view clustering (MVC) has gained broad attention owing to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). In specific, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works are proposed to handle it, but all fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address it. Specifically, it maintains a consensus coefficient matrix and updates knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. Besides, we design a three-step iterative algorithm to solve the resultant problem in linear complexity with proven convergence. Comprehensive experiments on various datasets show the superiority of FCMVC-IV.

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“…To the best of our knowledge, existing multi-view clustering algorithms can be divided into four categories including multi-view subspace clustering (Gao et al 2015;Liu et al 2021aLiu et al , 2022a, multiple kernel clustering (Zhang et al 2021;Li et al 2022a;Zhang et al 2022a;Wan et al 2022), graphbased methods (Liang et al 2020;Tang et al 2020;Liu et al 2022b,c;Xia et al 2022a), and matrix factorizationbased methods (Khan et al 2019;Gao et al 2019). Under the assumption that a linear combination of data samples can reconstruct themselves, multi-view subspace clustering obtains a reconstruction matrix upon a self-expressive framework.…”

Section: Introductionmentioning

confidence: 99%

Auto-Weighted Multi-View Clustering for Large-Scale Data

Wan

Liu

et al. 2023

AAAI

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Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factorization-based models are a representative of solving this problem. However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability. Moreover, a series of large-scale algorithms that bear one or more hyperparameters are impractical in real-world applications. To address the two issues, we propose an auto-weighted multi-view clustering (AWMVC) algorithm. Specifically, AWMVC first learns coefficient matrices from corresponding base matrices of different dimensions, then fuses them to obtain an optimal consensus matrix. By mapping original features into distinctive low-dimensional spaces, we can attain more comprehensive knowledge, thus obtaining better clustering results. Moreover, we design a six-step alternative optimization algorithm proven to be convergent theoretically. Also, AWMVC shows excellent performance on various benchmark datasets compared with existing ones. The code of AWMVC is publicly available at https://github.com/wanxinhang/AAAI-2023-AWMVC.

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Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph

Cited by 25 publications

References 59 publications

Attribute-Missing Graph Clustering Network

Attribute-Missing Graph Clustering Network

Continual Multi-view Clustering

Auto-Weighted Multi-View Clustering for Large-Scale Data

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