Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization
Nonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness. This study proposes a general semi-supervised...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020004 |
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| author | Guosheng Cui Ye Li Jianzhong Li Jianping Fan |
| author_facet | Guosheng Cui Ye Li Jianzhong Li Jianping Fan |
| author_sort | Guosheng Cui |
| collection | DOAJ |
| description | Nonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness. This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm. This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation, and adopts a feature normalizing strategy to align the different views. Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework: Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization (GDCMVNMF) and Extended Multi-View Constrained Nonnegative Matrix Factorization (ExMVCNMF). The intrinsic connection between these two specific implementations is discussed, and the optimization based on multiply update rules is presented. Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches. |
| format | Article |
| id | doaj-art-8e9d02b7e07c4f36bb68ef97b85131f3 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-8e9d02b7e07c4f36bb68ef97b85131f32025-02-03T07:26:26ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-03-0171557410.26599/BDMA.2023.9020004Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph RegularizationGuosheng Cui0Ye Li1Jianzhong Li2Jianping Fan3Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen 518055, ChinaSchool of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with University of Chinese Academy of Sciences, Beijing 100049, ChinaNonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness. This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm. This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation, and adopts a feature normalizing strategy to align the different views. Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework: Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization (GDCMVNMF) and Extended Multi-View Constrained Nonnegative Matrix Factorization (ExMVCNMF). The intrinsic connection between these two specific implementations is discussed, and the optimization based on multiply update rules is presented. Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.https://www.sciopen.com/article/10.26599/BDMA.2023.9020004multi-viewsemi-supervised clusteringdiscriminative informationgeometric informationfeature normalizing strategy |
| spellingShingle | Guosheng Cui Ye Li Jianzhong Li Jianping Fan Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization Big Data Mining and Analytics multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy |
| title | Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization |
| title_full | Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization |
| title_fullStr | Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization |
| title_full_unstemmed | Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization |
| title_short | Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization |
| title_sort | discriminatively constrained semi supervised multi view nonnegative matrix factorization with graph regularization |
| topic | multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020004 |
| work_keys_str_mv | AT guoshengcui discriminativelyconstrainedsemisupervisedmultiviewnonnegativematrixfactorizationwithgraphregularization AT yeli discriminativelyconstrainedsemisupervisedmultiviewnonnegativematrixfactorizationwithgraphregularization AT jianzhongli discriminativelyconstrainedsemisupervisedmultiviewnonnegativematrixfactorizationwithgraphregularization AT jianpingfan discriminativelyconstrainedsemisupervisedmultiviewnonnegativematrixfactorizationwithgraphregularization |