Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate cluster...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/15/8791 |
| _version_ | 1797587101362946048 |
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| author | Zonghan Shi Haitao Zhao |
| author_facet | Zonghan Shi Haitao Zhao |
| author_sort | Zonghan Shi |
| collection | DOAJ |
| description | Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of the self-expressive matrix increases quadratically with the number of input data points, making it difficult to handle large-scale datasets. Moreover, since multiple views are rich in information, both consistency and specificity of the input images need to be considered. To solve these problems, we propose a novel deep multi-view clustering approach based on the reconstructed self-expressive matrix (DCRSM). We use a reconstruction module to approximate self-expressive coefficients using only a small number of training samples, while the conventional self-expressive model must train the network with entire datasets. We also use shared layers and specific layers to integrate consistent and specific information of features to fuse information between views. The proposed DCRSM is extensively evaluated on multiple datasets, including Fashion-MNIST, COIL-20, COIL-100, and YTF. The experimental results demonstrate its superiority over several existing multi-view clustering methods, achieving an improvement between 1.94% and 4.2% in accuracy and a maximum improvement of 4.5% in NMI across different datasets. Our DCRSM also yields competitive results even when trained by 50% samples of the whole datasets. |
| first_indexed | 2024-03-11T00:31:31Z |
| format | Article |
| id | doaj.art-9b477e22da8b427aa15ebd64b5b2dcec |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:31:31Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9b477e22da8b427aa15ebd64b5b2dcec2023-11-18T22:37:35ZengMDPI AGApplied Sciences2076-34172023-07-011315879110.3390/app13158791Deep Multi-View Clustering Based on Reconstructed Self-Expressive MatrixZonghan Shi0Haitao Zhao1Automation Department, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaAutomation Department, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaDeep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of the self-expressive matrix increases quadratically with the number of input data points, making it difficult to handle large-scale datasets. Moreover, since multiple views are rich in information, both consistency and specificity of the input images need to be considered. To solve these problems, we propose a novel deep multi-view clustering approach based on the reconstructed self-expressive matrix (DCRSM). We use a reconstruction module to approximate self-expressive coefficients using only a small number of training samples, while the conventional self-expressive model must train the network with entire datasets. We also use shared layers and specific layers to integrate consistent and specific information of features to fuse information between views. The proposed DCRSM is extensively evaluated on multiple datasets, including Fashion-MNIST, COIL-20, COIL-100, and YTF. The experimental results demonstrate its superiority over several existing multi-view clustering methods, achieving an improvement between 1.94% and 4.2% in accuracy and a maximum improvement of 4.5% in NMI across different datasets. Our DCRSM also yields competitive results even when trained by 50% samples of the whole datasets.https://www.mdpi.com/2076-3417/13/15/8791multi-view learningsubspace clusteringself-expressive matrixdeep learning |
| spellingShingle | Zonghan Shi Haitao Zhao Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix Applied Sciences multi-view learning subspace clustering self-expressive matrix deep learning |
| title | Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix |
| title_full | Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix |
| title_fullStr | Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix |
| title_full_unstemmed | Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix |
| title_short | Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix |
| title_sort | deep multi view clustering based on reconstructed self expressive matrix |
| topic | multi-view learning subspace clustering self-expressive matrix deep learning |
| url | https://www.mdpi.com/2076-3417/13/15/8791 |
| work_keys_str_mv | AT zonghanshi deepmultiviewclusteringbasedonreconstructedselfexpressivematrix AT haitaozhao deepmultiviewclusteringbasedonreconstructedselfexpressivematrix |