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Multi-view clustering via neighbor domain correlation learning.

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Title: Multi-view clustering via neighbor domain correlation learning.
Authors: Li, Xiaocui1 (AUTHOR), Zhou, Ke1 (AUTHOR), Li, Chunhua1 (AUTHOR) li.chunhua@hust.edu.cn, Zhang, Xinyu2 (AUTHOR), Liu, Yu1 (AUTHOR), Wang, Yangtao1 (AUTHOR)
Source: Neural Computing & Applications. Apr2021, Vol. 33 Issue 8, p3403-3415. 13p.
Subjects: Computer engineering, Information commons, Data science, Neighbors
Abstract: With the development of data science, more and more data are presented in the form of multi-view. Compared with single-view feature learning, multi-view feature learning is more effective, and it has been successfully applied in many fields. Clustering is a core technology of computer science. Thus, many researchers start to study multi-view clustering. Recently, combining with multi-view feature learning techniques, some multi-view clustering methods have been presented. These methods mainly focus on the multiple features fusion, while most of them ignore the correlations among multiple views. Therefore, it cannot make full use of the advantages of multiple view features. In this paper, we propose a novel approach, named multi-view clustering via neighbor domain correlation learning (MCNDCL) approach. Specifically, MCNDCL learns a discriminant common space for multiple view features. Under the learned common space, the correlations of the consistent neighbor domain are maximized, and the correlations of specific neighbor domain are minimized at the same time. Extensive experimental results on four typical benchmarks, i.e., UCI Digits, Caltech7, BBCSport and CCV, validate the high effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: With the development of data science, more and more data are presented in the form of multi-view. Compared with single-view feature learning, multi-view feature learning is more effective, and it has been successfully applied in many fields. Clustering is a core technology of computer science. Thus, many researchers start to study multi-view clustering. Recently, combining with multi-view feature learning techniques, some multi-view clustering methods have been presented. These methods mainly focus on the multiple features fusion, while most of them ignore the correlations among multiple views. Therefore, it cannot make full use of the advantages of multiple view features. In this paper, we propose a novel approach, named multi-view clustering via neighbor domain correlation learning (MCNDCL) approach. Specifically, MCNDCL learns a discriminant common space for multiple view features. Under the learned common space, the correlations of the consistent neighbor domain are maximized, and the correlations of specific neighbor domain are minimized at the same time. Extensive experimental results on four typical benchmarks, i.e., UCI Digits, Caltech7, BBCSport and CCV, validate the high effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Apr2021
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