research-article

Multiview Clustering via Adaptively Weighted Procrustes

Authors:

Xuelong LiAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2022 - 2030

https://doi.org/10.1145/3219819.3220049

Published: 19 July 2018 Publication History

Abstract

In this paper, we make a multiview extension of the spectral rotation technique raised in single view spectral clustering research. Since spectral rotation is closely related to the Procrustes Analysis for points matching, we point out that classical Procrustes Average approach can be used for multiview clustering. Besides, we show that direct applying Procrustes Average (PA) in multiview tasks may not be optimal theoretically and empirically, since it does not take the clustering capacity differences of different views into consideration. Other than that, we propose an Adaptively Weighted Procrustes (AWP) approach to overcome the aforementioned deficiency. Our new AWP weights views with their clustering capacities and forms a weighted Procrustes Average problem accordingly. The optimization algorithm to solve the new model is computational complexity analyzed and convergence guaranteed. Experiments on five real-world datasets demonstrate the effectiveness and efficiency of the new models.

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Cited By

Kong DZhou SJin SYe FZhang X(2025)One-step multi-view spectral clustering based on multi-feature similarity fusionSignal Processing10.1016/j.sigpro.2024.109729227(109729)Online publication date: Mar-2025
https://doi.org/10.1016/j.sigpro.2024.109729
Zhou KZhu YGuo MJiang M(2025) MvWECM: Multi-view Weighted Evidential -Means clustering Pattern Recognition10.1016/j.patcog.2024.111108159(111108)Online publication date: Mar-2025
https://doi.org/10.1016/j.patcog.2024.111108
Yang YYu SPan BLi CLeung M(2024)Community Detection in Multiplex Networks Using Orthogonal Non-Negative Matrix Tri-Factorization Based on Graph Regularization and DiversityMathematics10.3390/math1208112412:8(1124)Online publication date: 9-Apr-2024
https://doi.org/10.3390/math12081124
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Index Terms

Multiview Clustering via Adaptively Weighted Procrustes
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

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Published: 19 July 2018

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National Natural Science Foundation of China

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Kong DZhou SJin SYe FZhang X(2025)One-step multi-view spectral clustering based on multi-feature similarity fusionSignal Processing10.1016/j.sigpro.2024.109729227(109729)Online publication date: Mar-2025
https://doi.org/10.1016/j.sigpro.2024.109729
Zhou KZhu YGuo MJiang M(2025) MvWECM: Multi-view Weighted Evidential -Means clustering Pattern Recognition10.1016/j.patcog.2024.111108159(111108)Online publication date: Mar-2025
https://doi.org/10.1016/j.patcog.2024.111108
Yang YYu SPan BLi CLeung M(2024)Community Detection in Multiplex Networks Using Orthogonal Non-Negative Matrix Tri-Factorization Based on Graph Regularization and DiversityMathematics10.3390/math1208112412:8(1124)Online publication date: 9-Apr-2024
https://doi.org/10.3390/math12081124
YOU YTANG CLIU XZOU XLIU YJIANG LZHANG C(2024)High-order graph fusion for multi-viewclusteringSCIENTIA SINICA Informationis10.1360/SSI-2023-021754:9(2098)Online publication date: 10-Sep-2024
https://doi.org/10.1360/SSI-2023-0217
Huang SCai HDai HFeng WLv JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Adaptive Instance-wise Multi-view ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681335(5299-5307)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681335
Li RHu HDu LChen JJiang BZhou PCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)One-Stage Fair Multi-View Spectral ClusteringProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681162(1407-1416)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681162
Gu ZFeng SLi ZYuan JLiu J(2024)NOODLE: Joint Cross-View Discrepancy Discovery and High-Order Correlation Detection for Multi-View Subspace ClusteringACM Transactions on Knowledge Discovery from Data10.1145/365330518:6(1-23)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3653305
Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645108
Gu ZLi ZFeng SBaeza-Yates RBonchi F(2024)Topology-Driven Multi-View Clustering via Tensorial Refined Sigmoid Rank MinimizationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672070(920-931)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672070
Ji JFeng SLi YBaeza-Yates RBonchi F(2024)Tensorized Unaligned Multi-view Clustering with Multi-scale Representation LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671689(1246-1256)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671689
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