research-article

Multi-view clustering with dual tensors

Authors:

Jian DaiAuthors Info & Claims

Neural Computing and Applications, Volume 34, Issue 10

Pages 8027 - 8038

https://doi.org/10.1007/s00521-022-06927-w

Published: 01 May 2022 Publication History

Abstract

Multi-view clustering methods based on tensor have achieved favorable performance thanks to the powerful capacity of capturing the high-order correlation hidden in multi-view data. However, many existing works only pay attention to exploring the inter-view correlation (i.e., the correlation between views for a same sample) and ignore the intra-view correlation (i.e., the correlation between different samples in a view), such that the high-order information cannot be fully utilized. Toward this issue, we propose an innovative multi-view clustering method in this paper, multi-view clustering with dual tensors (MCDT), which simultaneously exploits the intra-view correlation and the inter-view correlation. Specifically, we first learn a set of specific affinity matrices by using subspace learning in each view. Then, we stack these affinity matrices into a tensor and impose the tensor nuclear norm to exploit the intra-view high-order correlation. Meanwhile, we also rotate this tensor to exploit the inter-view high-order correlation, so as to exploit more comprehensive information hidden in multiple views. Extensive experiments on benchmark datasets demonstrate that the proposed MCDT obtains superior performance in comparison with existing state-of-the-art methods.

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Cited By

You XLi HYou JRen Z(2024)Consider high-order consistency for multi-view clusteringNeural Computing and Applications10.1007/s00521-023-09054-236:2(717-729)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00521-023-09054-2
Dai JSong HLuo YRen ZYang J(2023)Robust multi-view low-rank embedding clusteringNeural Computing and Applications10.1007/s00521-022-08137-w35:10(7877-7890)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s00521-022-08137-w
Zhang JFei LLi YNie FJiang QLiang LYan P(2022)Instance-level Weighted Graph Learning for Incomplete Multi-view ClusteringProceedings of the 2022 11th International Conference on Computing and Pattern Recognition10.1145/3581807.3581832(171-178)Online publication date: 17-Nov-2022
https://dl.acm.org/doi/10.1145/3581807.3581832
Show More Cited By

Index Terms

Multi-view clustering with dual tensors
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 34, Issue 10

May 2022

943 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 May 2022

Accepted: 04 January 2022

Received: 15 July 2021

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Sichuan Science and Technology Program
State Key Lab. Foundation for Novel Software Technology of Nanjing University
Sichuan Science and Technology Miaozi Program
National Statistical Science Research Project
Postgraduate Innovation Fund Project of Southwest University of Science and Technology
Guangxi Natural Science Foundation
Guangxi Science and Technology Major Project
National Natural Science Foundation of China
Natural Science Foundation Project of CQ CSTC

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Cited By

You XLi HYou JRen Z(2024)Consider high-order consistency for multi-view clusteringNeural Computing and Applications10.1007/s00521-023-09054-236:2(717-729)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s00521-023-09054-2
Dai JSong HLuo YRen ZYang J(2023)Robust multi-view low-rank embedding clusteringNeural Computing and Applications10.1007/s00521-022-08137-w35:10(7877-7890)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1007/s00521-022-08137-w
Zhang JFei LLi YNie FJiang QLiang LYan P(2022)Instance-level Weighted Graph Learning for Incomplete Multi-view ClusteringProceedings of the 2022 11th International Conference on Computing and Pattern Recognition10.1145/3581807.3581832(171-178)Online publication date: 17-Nov-2022
https://dl.acm.org/doi/10.1145/3581807.3581832
Liu LChen PLuo GKang ZLuo YHan S(2022)Scalable multi-view clustering with graph filteringNeural Computing and Applications10.1007/s00521-022-07326-x34:19(16213-16221)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s00521-022-07326-x

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