Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection
In multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect, posing a challenge to multiview clustering. A cl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/12/1606 |
| _version_ | 1827574852602560512 |
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| author | Ni Li Manman Peng Qiang Wu |
| author_facet | Ni Li Manman Peng Qiang Wu |
| author_sort | Ni Li |
| collection | DOAJ |
| description | In multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect, posing a challenge to multiview clustering. A clustering algorithm based on multiview feature selection clustering (MFSC), which combines similarity graph learning and unsupervised feature selection, is designed in this study. During the MFSC implementation, local manifold regularization is integrated into similarity graph learning, with the clustering label of similarity graph learning as the standard for unsupervised feature selection. MFSC can retain the characteristics of the clustering label on the premise of maintaining the manifold structure of multiview data. The algorithm is systematically evaluated using benchmark multiview and simulated data. The clustering experiment results prove that the MFSC algorithm is more effective than the traditional algorithm. |
| first_indexed | 2024-03-08T20:47:57Z |
| format | Article |
| id | doaj.art-9ea3cb10a4844d1bb669c1137f2f36d2 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-08T20:47:57Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-9ea3cb10a4844d1bb669c1137f2f36d22023-12-22T14:07:20ZengMDPI AGEntropy1099-43002023-11-012512160610.3390/e25121606Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature SelectionNi Li0Manman Peng1Qiang Wu2College of Information and Electronic Engineering, Hunan City University, Yiyang 413000, ChinaCollege of Information and Engineer, Hunan University, Changsha 410082, ChinaCollege of Information and Engineer, Hunan University, Changsha 410082, ChinaIn multiview data clustering, consistent or complementary information in the multiview data can achieve better clustering results. However, the high dimensions, lack of labeling, and redundancy of multiview data certainly affect the clustering effect, posing a challenge to multiview clustering. A clustering algorithm based on multiview feature selection clustering (MFSC), which combines similarity graph learning and unsupervised feature selection, is designed in this study. During the MFSC implementation, local manifold regularization is integrated into similarity graph learning, with the clustering label of similarity graph learning as the standard for unsupervised feature selection. MFSC can retain the characteristics of the clustering label on the premise of maintaining the manifold structure of multiview data. The algorithm is systematically evaluated using benchmark multiview and simulated data. The clustering experiment results prove that the MFSC algorithm is more effective than the traditional algorithm.https://www.mdpi.com/1099-4300/25/12/1606multiview data clusteringunsupervised feature selectionsimilarity graph |
| spellingShingle | Ni Li Manman Peng Qiang Wu Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection Entropy multiview data clustering unsupervised feature selection similarity graph |
| title | Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection |
| title_full | Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection |
| title_fullStr | Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection |
| title_full_unstemmed | Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection |
| title_short | Multiview Data Clustering with Similarity Graph Learning Guided Unsupervised Feature Selection |
| title_sort | multiview data clustering with similarity graph learning guided unsupervised feature selection |
| topic | multiview data clustering unsupervised feature selection similarity graph |
| url | https://www.mdpi.com/1099-4300/25/12/1606 |
| work_keys_str_mv | AT nili multiviewdataclusteringwithsimilaritygraphlearningguidedunsupervisedfeatureselection AT manmanpeng multiviewdataclusteringwithsimilaritygraphlearningguidedunsupervisedfeatureselection AT qiangwu multiviewdataclusteringwithsimilaritygraphlearningguidedunsupervisedfeatureselection |