Weighted Multi-view Clustering with Feature Selection

In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As different views are different representations of the same set of instances, we can simultaneously use information from multiple views to improve the...

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Published in:	Pattern recognition Vol. 53; pp. 25 - 35
Main Authors:	Xu, Yu-Meng, Wang, Chang-Dong, Lai, Jian-Huang
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.05.2016
Subjects:	Algorithms Clustering Data clustering Data points Feature selection Multi-view Pattern recognition Representations Weighting Feature selection Weighting Multi-view Data clustering
ISSN:	0031-3203, 1873-5142
Online Access:	Get full text
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Abstract	In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As different views are different representations of the same set of instances, we can simultaneously use information from multiple views to improve the clustering results generated by the limited information from a single view. Previous studies mainly focus on the relationships between distinct data views, which would get some improvement over the single-view clustering. However, in the case of high-dimensional data, where each view of data is of high dimensionality, feature selection is also a necessity for further improving the clustering results. To overcome this problem, this paper proposes a novel algorithm termed Weighted Multi-view Clustering with Feature Selection (WMCFS) that can simultaneously perform multi-view data clustering and feature selection. Two weighting schemes are designed that respectively weight the views of data points and feature representations in each view, such that the best view and the most representative feature space in each view can be selected for clustering. Experimental results conducted on real-world datasets have validated the effectiveness of the proposed method. •This paper proposes a new multi-view data clustering algorithm.•The new method considers both view weighting and feature weighting.•An EM-like method is designed to get the local optimum solution.•Extensive experiments have been conducted to show the effectiveness.
AbstractList	In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As different views are different representations of the same set of instances, we can simultaneously use information from multiple views to improve the clustering results generated by the limited information from a single view. Previous studies mainly focus on the relationships between distinct data views, which would get some improvement over the single-view clustering. However, in the case of high-dimensional data, where each view of data is of high dimensionality, feature selection is also a necessity for further improving the clustering results. To overcome this problem, this paper proposes a novel algorithm termed Weighted Multi-view Clustering with Feature Selection (WMCFS) that can simultaneously perform multi-view data clustering and feature selection. Two weighting schemes are designed that respectively weight the views of data points and feature representations in each view, such that the best view and the most representative feature space in each view can be selected for clustering. Experimental results conducted on real-world datasets have validated the effectiveness of the proposed method. In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As different views are different representations of the same set of instances, we can simultaneously use information from multiple views to improve the clustering results generated by the limited information from a single view. Previous studies mainly focus on the relationships between distinct data views, which would get some improvement over the single-view clustering. However, in the case of high-dimensional data, where each view of data is of high dimensionality, feature selection is also a necessity for further improving the clustering results. To overcome this problem, this paper proposes a novel algorithm termed Weighted Multi-view Clustering with Feature Selection (WMCFS) that can simultaneously perform multi-view data clustering and feature selection. Two weighting schemes are designed that respectively weight the views of data points and feature representations in each view, such that the best view and the most representative feature space in each view can be selected for clustering. Experimental results conducted on real-world datasets have validated the effectiveness of the proposed method. •This paper proposes a new multi-view data clustering algorithm.•The new method considers both view weighting and feature weighting.•An EM-like method is designed to get the local optimum solution.•Extensive experiments have been conducted to show the effectiveness.
Author	Wang, Chang-Dong Lai, Jian-Huang Xu, Yu-Meng
Author_xml	– sequence: 1 givenname: Yu-Meng surname: Xu fullname: Xu, Yu-Meng email: yumengxu@hotmail.com organization: School of Information Science and Technology, Sun Yat-sen University, Guangzhou, PR China – sequence: 2 givenname: Chang-Dong surname: Wang fullname: Wang, Chang-Dong email: changdongwang@hotmail.com organization: School of Mobile Information Engineering, Sun Yat-sen University, Zhuhai, PR China – sequence: 3 givenname: Jian-Huang surname: Lai fullname: Lai, Jian-Huang email: stsljh@mail.sysu.edu.cn organization: School of Information Science and Technology, Sun Yat-sen University, Guangzhou, PR China
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Snippet	In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As...
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SubjectTerms	Algorithms Clustering Data clustering Data points Feature selection Multi-view Pattern recognition Representations Weighting
Title	Weighted Multi-view Clustering with Feature Selection
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