research-article

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

Authors:

Songhe FengAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 1

Article No.: 7, Pages 1 - 21

https://doi.org/10.1145/3532612

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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Kumar AYadav J(2025)Unveiling the potential of graph coloring in feature set partitioning: A study on high-dimensional datasetsNeurocomputing10.1016/j.neucom.2024.128814614(128814)Online publication date: Jan-2025
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Index Terms

Individuality Meets Commonality: A Unified Graph Learning Framework for Multi-View Clustering
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning algorithms
2. Information systems
  1. Information systems applications
    1. Data mining

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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

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

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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

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Cited By

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https://doi.org/10.1016/j.neucom.2025.129568
Kumar AYadav J(2025)Unveiling the potential of graph coloring in feature set partitioning: A study on high-dimensional datasetsNeurocomputing10.1016/j.neucom.2024.128814614(128814)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128814
Meng JZhu ZQi JZhang H(2025)DRSS: a multimodal sentiment analysis approach based on dual representation and self-supervised learning strategyThe Journal of Supercomputing10.1007/s11227-024-06524-981:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06524-9
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
Wang YPei XZhan H(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
https://doi.org/10.1109/TETCI.2023.3306027
Zhou SSong P(2024)Consistency–exclusivity guided unsupervised multi-view feature selectionNeurocomputing10.1016/j.neucom.2023.127119569:COnline publication date: 14-Mar-2024
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Chen BRen XBai SChen ZZheng QZhu J(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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Zhou BJi JGu ZZhou ZDing GFeng S(2024)One-step graph-based incomplete multi-view clusteringMultimedia Systems10.1007/s00530-023-01225-430:1Online publication date: 19-Jan-2024
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Zhao JWang XZou QKang FWang FPeng J(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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