Multiview Deep Autoencoder-Inspired Layerwise Error-Correcting Non-Negative Matrix Factorization
Multiview Clustering (MVC) plays a crucial role in the holistic analysis of complex data by leveraging complementary information from multiple perspectives, a necessity in the era of big data. Non-negative Matrix Factorization (NMF)-based methods have demonstrated their effectiveness and broad appli...
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MDPI AG
2025-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/9/1422 |
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| author | Yuan Liu Yuan Wan Zaili Yang Huanhuan Li |
| author_facet | Yuan Liu Yuan Wan Zaili Yang Huanhuan Li |
| author_sort | Yuan Liu |
| collection | DOAJ |
| description | Multiview Clustering (MVC) plays a crucial role in the holistic analysis of complex data by leveraging complementary information from multiple perspectives, a necessity in the era of big data. Non-negative Matrix Factorization (NMF)-based methods have demonstrated their effectiveness and broad applicability in clustering tasks, as they generate meaningful attribute distributions and cluster assignments. However, existing shallow NMF approaches fail to capture the hierarchical structures inherent in real-world data, while deep NMF ones overlook the accumulation of reconstruction errors across layers by solely focusing on a global loss function. To address these limitations, this study aims to develop a novel method that integrates an autoencoder-inspired structure into the deep NMF framework, incorporating layerwise error-correcting constraints. This approach can facilitate the extraction of hierarchical features while effectively mitigating reconstruction error accumulation in deep architectures. Additionally, repulsion-attraction manifold learning is incorporated at each layer to preserve intrinsic geometric structures within the data. The proposed model is evaluated on five real-world multiview datasets, with experimental results demonstrating its effectiveness in capturing hierarchical representations and improving clustering performance. |
| format | Article |
| id | doaj-art-200cf25343da4a83ba2248e31b9b0537 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-200cf25343da4a83ba2248e31b9b05372025-08-20T02:24:47ZengMDPI AGMathematics2227-73902025-04-01139142210.3390/math13091422Multiview Deep Autoencoder-Inspired Layerwise Error-Correcting Non-Negative Matrix FactorizationYuan Liu0Yuan Wan1Zaili Yang2Huanhuan Li3School of Mathematics and Statistics, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, ChinaSchool of Mathematics and Statistics, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, ChinaLiverpool Logistics, Offshore and Marine Research Institute, Liverpool John Moores University, Liverpool L3 3AF, UKLiverpool Logistics, Offshore and Marine Research Institute, Liverpool John Moores University, Liverpool L3 3AF, UKMultiview Clustering (MVC) plays a crucial role in the holistic analysis of complex data by leveraging complementary information from multiple perspectives, a necessity in the era of big data. Non-negative Matrix Factorization (NMF)-based methods have demonstrated their effectiveness and broad applicability in clustering tasks, as they generate meaningful attribute distributions and cluster assignments. However, existing shallow NMF approaches fail to capture the hierarchical structures inherent in real-world data, while deep NMF ones overlook the accumulation of reconstruction errors across layers by solely focusing on a global loss function. To address these limitations, this study aims to develop a novel method that integrates an autoencoder-inspired structure into the deep NMF framework, incorporating layerwise error-correcting constraints. This approach can facilitate the extraction of hierarchical features while effectively mitigating reconstruction error accumulation in deep architectures. Additionally, repulsion-attraction manifold learning is incorporated at each layer to preserve intrinsic geometric structures within the data. The proposed model is evaluated on five real-world multiview datasets, with experimental results demonstrating its effectiveness in capturing hierarchical representations and improving clustering performance.https://www.mdpi.com/2227-7390/13/9/1422multiview clustering (MVC)autoencoder-inspired structurenon-negative matrix factorization (NMF)geometric information |
| spellingShingle | Yuan Liu Yuan Wan Zaili Yang Huanhuan Li Multiview Deep Autoencoder-Inspired Layerwise Error-Correcting Non-Negative Matrix Factorization Mathematics multiview clustering (MVC) autoencoder-inspired structure non-negative matrix factorization (NMF) geometric information |
| title | Multiview Deep Autoencoder-Inspired Layerwise Error-Correcting Non-Negative Matrix Factorization |
| title_full | Multiview Deep Autoencoder-Inspired Layerwise Error-Correcting Non-Negative Matrix Factorization |
| title_fullStr | Multiview Deep Autoencoder-Inspired Layerwise Error-Correcting Non-Negative Matrix Factorization |
| title_full_unstemmed | Multiview Deep Autoencoder-Inspired Layerwise Error-Correcting Non-Negative Matrix Factorization |
| title_short | Multiview Deep Autoencoder-Inspired Layerwise Error-Correcting Non-Negative Matrix Factorization |
| title_sort | multiview deep autoencoder inspired layerwise error correcting non negative matrix factorization |
| topic | multiview clustering (MVC) autoencoder-inspired structure non-negative matrix factorization (NMF) geometric information |
| url | https://www.mdpi.com/2227-7390/13/9/1422 |
| work_keys_str_mv | AT yuanliu multiviewdeepautoencoderinspiredlayerwiseerrorcorrectingnonnegativematrixfactorization AT yuanwan multiviewdeepautoencoderinspiredlayerwiseerrorcorrectingnonnegativematrixfactorization AT zailiyang multiviewdeepautoencoderinspiredlayerwiseerrorcorrectingnonnegativematrixfactorization AT huanhuanli multiviewdeepautoencoderinspiredlayerwiseerrorcorrectingnonnegativematrixfactorization |