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Individuality Meets Commonality: A Unified Graph Learning Framework for Multi-View Clustering

Published: 20 February 2023 Publication History

Abstract

Multi-view clustering, which aims at boosting the clustering performance by leveraging the individual information and the common information of multi-view data, has gained extensive consideration in recent years. However, most existing multi-view clustering algorithms either focus on extracting the multi-view individuality or emphasize on exploring the multi-view commonality, neither of which can fully utilize the comprehensive information from multiple views. To this end, we propose a novel algorithm named View-specific and Consensus Graph Alignment (VCGA) for multi-view clustering, which simultaneously formulates the multi-view individuality and the multi-view commonality into a unified framework to effectively partition data points. To be specific, the VCGA model constructs the view-specific graphs and the shared graph from original multi-view data and hidden latent representation, respectively. Furthermore, the view-specific graphs of different views and the consensus graph are aligned into an informative target graph, which is employed as a crucial input to the standard spectral clustering method for clustering. Extensive experimental results on six benchmark datasets demonstrate the superiority of our method against other state-of-the-art clustering algorithms.

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        Published In

        cover image ACM Transactions on Knowledge Discovery from Data
        ACM Transactions on Knowledge Discovery from Data  Volume 17, Issue 1
        January 2023
        375 pages
        ISSN:1556-4681
        EISSN:1556-472X
        DOI:10.1145/3572846
        Issue’s Table of Contents

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 20 February 2023
        Online AM: 27 April 2022
        Accepted: 16 April 2022
        Revised: 11 March 2022
        Received: 02 November 2021
        Published in TKDD Volume 17, Issue 1

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        Author Tags

        1. Multi-view clustering
        2. individuality and commonality
        3. local structured graph learning
        4. self-representation

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        • Research-article

        Funding Sources

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

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        • (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
        • (2024)Fine-Grained Graph Learning for Multi-View Subspace ClusteringIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.33060278:4(2804-2815)Online publication date: Aug-2024
        • (2024)Consistency–exclusivity guided unsupervised multi-view feature selectionNeurocomputing10.1016/j.neucom.2023.127119569:COnline publication date: 14-Mar-2024
        • (2024)Multi-view representation learning with dual-label collaborative guidanceKnowledge-Based Systems10.1016/j.knosys.2024.112680305(112680)Online publication date: Dec-2024
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        • (2023)A multiple kinds of information extraction method for multi-view low-rank subspace clusteringInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01969-515:4(1313-1330)Online publication date: 8-Oct-2023

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