Instance-specific canonical correlation analysis

doi:10.1016/j.neucom.2014.12.028

CORC > 北京大学 > 信息科学技术学院

	Instance-specific canonical correlation analysis
	Zhai, Deming ; Zhang, Yu ; Yeung, Dit-Yan ; Chang, Hong ; Chen, Lin ; Gao, Wen
刊名	neurocomputing
	2015
关键词	Canonical correlation analysis Least squares regression Multi-view statistical learning LEAST-SQUARES MODELS
DOI	10.1016/j.neucom.2014.12.028
英文摘要	Canonical Correlation Analysis (CCA) is one of the most popular statistical methods to capture the correlations between two variables. However, it has limitations as a linear and global algorithm. Although some variants have been proposed to overcome the limitations, neither of them achieves locality and nonlinearity at the same time. In this paper, we propose a novel algorithm called Instance-Specific Canonical Correlation Analysis (ISCCA), which approximates the nonlinear data by computing the instance-specific projections along the smooth curve of the manifold. First, we propose a least squares solution for CCA which will set the stage for the proposed method. Second, based on the framework of least squares regression, CCA is extended to the instance-specific case which obtains a set of locally linear smooth but globally nonlinear transformations. Third, ISCCA can be extended to semi-supervised setting by exploiting the unlabeled data to further improve the performance. The optimization problem is proved to be convex and could be solved efficiently by alternating optimization. And the globally optimal solutions could be achieved with theoretical guarantee. Moreover, for large scale applications, iterative conjugate gradient algorithm can be used to speed up the computation procedure. Experimental results demonstrate the effectiveness of our proposed method. (C) 2014 Elsevier B.V. All rights reserved.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000350934600023&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; SCI(E); EI; 1; ARTICLE; zhaideming@hit.edu.cn; 205-218; 155
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/206992]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhai, Deming,Zhang, Yu,Yeung, Dit-Yan,et al. Instance-specific canonical correlation analysis[J]. neurocomputing,2015.
APA	Zhai, Deming,Zhang, Yu,Yeung, Dit-Yan,Chang, Hong,Chen, Lin,&Gao, Wen.(2015).Instance-specific canonical correlation analysis.neurocomputing.
MLA	Zhai, Deming,et al."Instance-specific canonical correlation analysis".neurocomputing (2015).