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Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization

Zengfa Dou, Nian Peng, Weiming Hou, Xianghua Xie Orcid Logo, Xiaoke Ma Orcid Logo

Neural Networks, Volume: 182, Start page: 106856

Swansea University Author: Xianghua Xie Orcid Logo

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DOI (Published version): 10.1016/j.neunet.2024.106856

Abstract

Clustering of multi-view data divides objects into groups by preserving structure of clusters in all views, requiring simultaneously takes into consideration diversity and consistency of various views, corresponding to the shared and specific components of various views. Current algorithms fail to f...

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Published in: Neural Networks
ISSN: 0893-6080 1879-2782
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68375
first_indexed 2024-11-28T19:47:26Z
last_indexed 2025-01-13T20:34:26Z
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spelling 2025-01-13T13:18:32.5268482 v2 68375 2024-11-28 Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2024-11-28 MACS Clustering of multi-view data divides objects into groups by preserving structure of clusters in all views, requiring simultaneously takes into consideration diversity and consistency of various views, corresponding to the shared and specific components of various views. Current algorithms fail to fully characterize and balance diversity and consistency of various views, resulting in the undesirable performance. Here, a novel Multi-View Clustering with Deep non-negative matrix factorization and Multi-Level Representation (MVC-DMLR) learning is proposed, which integrates feature learning, multi-level topology representation, and clustering of multi-view data. Specifically, MVC-DMLR first learns multi-level representation (also called deep features) of objects with deep nonnegative matrix factorization (DNMF), facilitating the exploitation of hierarchical structure of multi-view data. Then, it learns multi-level graphs for each view from multi-level representation, where relations between diversity and consistency are addressed at various resolutions. MVC-DMLR integrates multi-level representation learning, multi-level topology representation learning and clustering, which is formulated as an optimization problem. Experimental results show the superiority of MVC-DMLR to baselines in terms of accuracy, F1-score, normalized mutual information and adjusted rand index. Journal Article Neural Networks 182 106856 Elsevier BV 0893-6080 1879-2782 Multi-view clustering, deep non-negative matrix factorization, self-representation learning, multi-layer networks 1 2 2025 2025-02-01 10.1016/j.neunet.2024.106856 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required This work was supported by the National Natural Science Foundation of China (62272361). 2025-01-13T13:18:32.5268482 2024-11-28T14:30:02.2505143 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Zengfa Dou 1 Nian Peng 2 Weiming Hou 3 Xianghua Xie 0000-0002-2701-8660 4 Xiaoke Ma 0000-0002-5604-7137 5
title Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization
spellingShingle Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization
Xianghua Xie
title_short Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization
title_full Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization
title_fullStr Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization
title_full_unstemmed Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization
title_sort Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Xianghua Xie
author2 Zengfa Dou
Nian Peng
Weiming Hou
Xianghua Xie
Xiaoke Ma
format Journal article
container_title Neural Networks
container_volume 182
container_start_page 106856
publishDate 2025
institution Swansea University
issn 0893-6080
1879-2782
doi_str_mv 10.1016/j.neunet.2024.106856
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
description Clustering of multi-view data divides objects into groups by preserving structure of clusters in all views, requiring simultaneously takes into consideration diversity and consistency of various views, corresponding to the shared and specific components of various views. Current algorithms fail to fully characterize and balance diversity and consistency of various views, resulting in the undesirable performance. Here, a novel Multi-View Clustering with Deep non-negative matrix factorization and Multi-Level Representation (MVC-DMLR) learning is proposed, which integrates feature learning, multi-level topology representation, and clustering of multi-view data. Specifically, MVC-DMLR first learns multi-level representation (also called deep features) of objects with deep nonnegative matrix factorization (DNMF), facilitating the exploitation of hierarchical structure of multi-view data. Then, it learns multi-level graphs for each view from multi-level representation, where relations between diversity and consistency are addressed at various resolutions. MVC-DMLR integrates multi-level representation learning, multi-level topology representation learning and clustering, which is formulated as an optimization problem. Experimental results show the superiority of MVC-DMLR to baselines in terms of accuracy, F1-score, normalized mutual information and adjusted rand index.
published_date 2025-02-01T20:36:25Z
_version_ 1821348604479012864
score 11.04748

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