Journal article 90 views
Learning multi-level topology representation for multi-view clustering with deep non-negative matrix factorization
Zengfa Dou,
Nian Peng,
Weiming Hou,
Xianghua Xie 
,
Xiaoke Ma
,
Xiaoke Ma
Neural Networks, Volume: 182, Start page: 106856
Swansea University Author:
Xianghua Xie
Full text not available from this repository: check for access using links below.
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...
| Published in: | Neural Networks |
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| ISSN: | 0893-6080 1879-2782 |
| Published: |
Elsevier BV
2025
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| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa68375 |
| 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 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. |
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| Keywords: |
Multi-view clustering, deep non-negative matrix factorization, self-representation learning, multi-layer networks |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work was supported by the National Natural Science Foundation of China (62272361). |
| Start Page: |
106856 |