Robust Graph-Based Multi-View Clustering

Liang, Weixuan; Liu, Xinwang; Zhou, Sihang; Liu, Jiyuan; Wang, Siwei; Zhu, En

doi:10.1609/aaai.v36i7.20710

Cited by 19 publications

(3 citation statements)

References 71 publications

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“…The Thirty-Eighth AAAI Conference on Artificial Intelligence clustering results. Liang et al (Liang et al 2022) proposed an robust and parameter-free parametric graph-based multiview clustering method. They defined a convex optimization graph-based multi-view clustering formulation, and used a gradient descent-based algorithm to solve the resulting optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Discriminatively Fuzzy Multi-View K-means Clustering with Local Structure Preserving

Yin,

Sun,

Wei

et al. 2024

AAAI

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Multi-view K-means clustering successfully generalizes K-means from single-view to multi-view, and obtains excellent clustering performance. In every view, it makes each data point close to the center of the corresponding cluster. However, multi-view K-means only considers the compactness of each cluster, but ignores the separability of different clusters, which is of great importance to producing a good clustering result. In this paper, we propose Discriminatively Fuzzy Multi-view K-means clustering with Local Structure Preserving (DFMKLS). On the basis of minimizing the distance between each data point and the center of the corresponding cluster, DFMKLS separates clusters by maximizing the distance between the centers of pairwise clusters. DFMKLS also relaxes its objective by introducing the idea of fuzzy clustering, which calculates the probability that a data point belongs to each cluster. Considering multi-view K-means mainly focuses on the global information of the data, to efficiently use the local information, we integrate the local structure preserving into the framework of DFMKLS. The effectiveness of DFMKLS is evaluated on benchmark multi-view datasets. It obtains superior performances than state-of-the-art multi-view clustering methods, including multi-view K-means.

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Section: Introductionmentioning

confidence: 99%

Discriminatively Fuzzy Multi-View K-means Clustering with Local Structure Preserving

Yin,

Sun,

Wei

et al. 2024

AAAI

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“…In the big data era, the exploration of a comprehensive understanding of heterogeneous features such as images, videos, speech, and text is an important research problem (Wu et al 2023). Multi-view clustering (MVC) algorithms (Ling et al 2023;Xu et al 2023) have found widespread application across many fields. Benefiting from the modeling capacity of deep neural networks, Deep Multi-view Clustering (DMVC) (Li et al 2019;Yang et al 2022) methods emerge to provide an effective solution for handling highdimensional and complex multi-view data.…”

Section: Introductionmentioning

confidence: 99%

SURER: Structure-Adaptive Unified Graph Neural Network for Multi-View Clustering

Wang,

Feng,

Lyu

et al. 2024

AAAI

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Deep Multi-view Graph Clustering (DMGC) aims to partition instances into different groups using the graph information extracted from multi-view data. The mainstream framework of DMGC methods applies graph neural networks to embed structure information into the view-specific representations and fuse them for the consensus representation. However, on one hand, we find that the graph learned in advance is not ideal for clustering as it is constructed by original multi-view data and localized connecting. On the other hand, most existing methods learn the consensus representation in a late fusion manner, which fails to propagate the structure relations across multiple views. Inspired by the observations, we propose a Structure-adaptive Unified gRaph nEural network for multi-view clusteRing (SURER), which can jointly learn a heterogeneous multi-view unified graph and robust graph neural networks for multi-view clustering. Specifically, we first design a graph structure learning module to refine the original view-specific attribute graphs, which removes false edges and discovers the potential connection. According to the view-specific refined attribute graphs, we integrate them into a unified heterogeneous graph by linking the representations of the same sample from different views. Furthermore, we use the unified heterogeneous graph as the input of the graph neural network to learn the consensus representation for each instance, effectively integrating complementary information from various views. Extensive experiments on diverse datasets demonstrate the superior effectiveness of our method compared to other state-of-the-art approaches.

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“…subspace representation through the learning and comparison of different views (Yang et al 2022;Huang et al 2021;Tan et al 2021;Xu et al 2021). The latter usually focuses on mining graph structure information in features and obtaining assignment results via Laplace matrix eigendecomposition (Liang et al 2022;Qiang et al 2021;Zhong and Pun 2022). In recent years, MVC methods achieve impressive performance.…”

Section: Introductionmentioning

confidence: 99%

Dual Label-Guided Graph Refinement for Multi-View Graph Clustering

Ling

Chen

et al. 2023

AAAI

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With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can discover the hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to the given graphs, especially influenced by the low quality graphs, i.e., they tend to be limited by the homophily assumption. However, the widespread real-world data hardly satisfy the homophily assumption. This gap limits the performance of existing MVGC methods on low homophilous graphs. To mitigate this limitation, our motivation is to extract high-level view-common information which is used to refine each view's graph, and reduce the influence of non-homophilous edges. To this end, we propose dual label-guided graph refinement for multi-view graph clustering (DuaLGR), to alleviate the vulnerability in facing low homophilous graphs. Specifically, DuaLGR consists of two modules named dual label-guided graph refinement module and graph encoder module. The first module is designed to extract the soft label from node features and graphs, and then learn a refinement matrix. In cooperation with the pseudo label from the second module, these graphs are refined and aggregated adaptively with different orders. Subsequently, a consensus graph can be generated in the guidance of the pseudo label. Finally, the graph encoder module encodes the consensus graph along with node features to produce the high-level pseudo label for iteratively clustering. The experimental results show the superior performance on coping with low homophilous graph data. The source code for DuaLGR is available at https://github.com/YwL-zhufeng/DuaLGR.

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Robust Graph-Based Multi-View Clustering

Cited by 19 publications

References 71 publications

Discriminatively Fuzzy Multi-View K-means Clustering with Local Structure Preserving

Discriminatively Fuzzy Multi-View K-means Clustering with Local Structure Preserving

SURER: Structure-Adaptive Unified Graph Neural Network for Multi-View Clustering

Dual Label-Guided Graph Refinement for Multi-View Graph Clustering

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