research-article

Structure Diversity-Induced Anchor Graph Fusion for Multi-View Clustering

Authors:

Songhe FengAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 2

Article No.: 17, Pages 1 - 18

https://doi.org/10.1145/3534931

Published: 20 February 2023 Publication History

Abstract

The anchor graph structure has been widely used to speed up large-scale multi-view clustering and exhibited promising performance. How to effectively integrate the anchor graphs on multiple views to achieve enhanced clustering performance still remains a challenging task. Existing fusing strategies ignore the structure diversity among anchor graphs and restrict the anchor generation to be same on different views, which degenerates the representation ability of corresponding fused consensus graph. To overcome these drawbacks, we propose a novel structural fusion framework to integrate the multi-view anchor graphs for clustering. Different from traditional integration strategies, we merge the anchors and edges of all the view-specific anchor graphs into a single graph for the structural optimal graph learning. Benefiting from the structural fusion strategy, the anchor generation of each view is not forced to be same, which greatly improves the representation capability of the target structural optimal graph, since the anchors of each view capture the diverse structure of different views. By leveraging the potential structural consistency among each anchor graph, a connectivity constraint is imposed on the target graph to indicate clusters directly without any post-processing such as k-means in classical spectral clustering. Substantial experiments on real-world datasets are conducted to verify the superiority of the proposed method, as compared with the state-of-the-arts over the clustering performance and time expenditure.

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Cited By

Yu XLiu HZhang YLin YZhang C(2024)Hubness-Enabled Clustering and Recovery for Large-Scale Incomplete Multi-View DataACM Transactions on Knowledge Discovery from Data10.1145/369468919:1(1-23)Online publication date: 4-Sep-2024
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Fang SHuang DCai XWang CHe CTang Y(2024)Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.326146035:8(11436-11447)Online publication date: Aug-2024
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Yu SWang SWen YWang ZLuo ZZhu ELiu X(2024)How to Construct Corresponding Anchors for Incomplete Multiview ClusteringIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.331297934:4(2845-2860)Online publication date: Apr-2024
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Index Terms

Structure Diversity-Induced Anchor Graph Fusion for Multi-View Clustering
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 2

February 2023

355 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3572847

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 February 2023

Online AM: 05 May 2022

Accepted: 30 April 2022

Revised: 13 March 2022

Received: 15 November 2021

Published in TKDD Volume 17, Issue 2

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National Natural Science Foundation of China
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National Key Research and Development Project

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Cited By

Yu XLiu HZhang YLin YZhang C(2024)Hubness-Enabled Clustering and Recovery for Large-Scale Incomplete Multi-View DataACM Transactions on Knowledge Discovery from Data10.1145/369468919:1(1-23)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3694689
Fang SHuang DCai XWang CHe CTang Y(2024)Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.326146035:8(11436-11447)Online publication date: Aug-2024
https://doi.org/10.1109/TNNLS.2023.3261460
Yu SWang SWen YWang ZLuo ZZhu ELiu X(2024)How to Construct Corresponding Anchors for Incomplete Multiview ClusteringIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.331297934:4(2845-2860)Online publication date: Apr-2024
https://doi.org/10.1109/TCSVT.2023.3312979
Cui GHao FWu DLi Y(2024)Fast label prediction based on shrunk anchor graph for semi-supervised incomplete multiview classification2024 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME57554.2024.10688009(1-6)Online publication date: 15-Jul-2024
https://doi.org/10.1109/ICME57554.2024.10688009
Liu SLiang KDong ZWang SYang XZhou SZhu ELiu X(2024)Learn from View Correlation: An Anchor Enhancement Strategy for Multi-View Clustering2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02471(26151-26161)Online publication date: 16-Jun-2024
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Li YHu XZhu TLiu JLiu XLiu Z(2024)Discriminative embedded multi-view fuzzy C-means clustering for feature-redundant and incomplete dataInformation Sciences10.1016/j.ins.2024.120830677(120830)Online publication date: Aug-2024
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Li LZhang JWang SLiu XLi KLi K(2023)Multi-View Bipartite Graph Clustering With Coupled Noisy Feature FilterIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326821535:12(12842-12854)Online publication date: 27-Apr-2023
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Su CYuan HLai LYang Q(2023)Anchor-based multi-view subspace clustering with graph learningNeurocomputing10.1016/j.neucom.2023.126320547(126320)Online publication date: Aug-2023
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