Multi-view dimensionality reduction via canonical random correlation analysis

doi:10.1007/s11704-015-4538-7

Front. Comput. Sci.

2016, Vol. 10

Issue (5) : 856-869 https://doi.org/10.1007/s11704-015-4538-7

RESEARCH ARTICLE

Multi-view dimensionality reduction via canonical random correlation analysis

Yanyan ZHANG¹,Jianchun ZHANG²,Zhisong PAN¹(

),Daoqiang ZHANG²(

)

¹. College of Command Information Systems, PLA University of Science and Technology, Nanjing 210007, China
². Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

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Abstract

Canonical correlation analysis (CCA) is one of the most well-known methods to extract features from multiview data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take discriminant information into account. In this paper, we add discriminant information into CCA by using random cross view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis (RCA). In RCA, two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique. Furthermore, kernel RCA (KRCA) is proposed to extract nonlinear correlations between different views. Experiments on several multi-view data sets show the effectiveness of the proposed methods.

Keywords canonical correlation analysis discriminant multi-view dimensionality reduction

Corresponding Author(s): Zhisong PAN,Daoqiang ZHANG

Just Accepted Date: 10 October 2015 Online First Date: 06 April 2016 Issue Date: 07 September 2016

Cite this article:

Yanyan ZHANG,Jianchun ZHANG,Zhisong PAN, et al. Multi-view dimensionality reduction via canonical random correlation analysis[J]. Front. Comput. Sci., 2016, 10(5): 856-869.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-015-4538-7
https://academic.hep.com.cn/fcs/EN/Y2016/V10/I5/856

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