GMC: Graph-Based Multi-View Clustering
Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objec...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering Jg. 32; H. 6; S. 1116 - 1129 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
IEEE
01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1041-4347, 1558-2191 |
| Online-Zugang: | Volltext |
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| Abstract | Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general G raph-based M ulti-view C lustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified graph matrix. The unified graph matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its learning method, which can help the learning of each view graph matrix and the learning of the unified graph matrix in a mutual reinforcement manner. A novel multi-view fusion technique can automatically weight each data graph matrix to derive the unified graph matrix. A rank constraint without introducing a tuning parameter is also imposed on the graph Laplacian matrix of the unified matrix, which helps partition the data points naturally into the required number of clusters. An alternating iterative optimization algorithm is presented to optimize the objective function. Experimental results using both toy data and real-world data demonstrate that the proposed method outperforms state-of-the-art baselines markedly. |
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| AbstractList | Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general G raph-based M ulti-view C lustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified graph matrix. The unified graph matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its learning method, which can help the learning of each view graph matrix and the learning of the unified graph matrix in a mutual reinforcement manner. A novel multi-view fusion technique can automatically weight each data graph matrix to derive the unified graph matrix. A rank constraint without introducing a tuning parameter is also imposed on the graph Laplacian matrix of the unified matrix, which helps partition the data points naturally into the required number of clusters. An alternating iterative optimization algorithm is presented to optimize the objective function. Experimental results using both toy data and real-world data demonstrate that the proposed method outperforms state-of-the-art baselines markedly. |
| Author | Yang, Yan Liu, Bing Wang, Hao |
| Author_xml | – sequence: 1 givenname: Hao orcidid: 0000-0001-9492-3807 surname: Wang fullname: Wang, Hao email: cshaowang@gmail.com organization: School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China – sequence: 2 givenname: Yan orcidid: 0000-0002-6134-6094 surname: Yang fullname: Yang, Yan email: yyang@swjtu.edu.cn organization: School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China – sequence: 3 givenname: Bing surname: Liu fullname: Liu, Bing email: liub@uic.edu organization: Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA |
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| CODEN | ITKEEH |
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| Snippet | Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration... |
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| SubjectTerms | Algorithms Clustering Clustering algorithms Clustering methods Computational complexity Computational modeling data fusion Data points graph-based clustering Kernel Laplace equations Laplacian matrix Learning Matrix converters Multi-view clustering Optimization rank constraint |
| Title | GMC: Graph-Based Multi-View Clustering |
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