Multi-View Discriminant Analysis

doi:10.1109/TPAMI.2015.2435740

	Multi-View Discriminant Analysis
	Kan, Meina 1; Shan, Shiguang 1; Zhang, Haihong 2; Lao, Shihong 2; Chen, Xilin 1
	2016
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	38 期号:1 页码:188-194
摘要	In many computer vision systems, the same object can be observed at varying viewpoints or even by different sensors, which brings in the challenging demand for recognizing objects from distinct even heterogeneous views. In this work we propose a Multi-view Discriminant Analysis (MvDA) approach, which seeks for a single discriminant common space for multiple views in a non-pairwise manner by jointly learning multiple view-specific linear transforms. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, the multiple linear transforms are achieved analytically and simultaneously through generalized eigenvalue decomposition. Furthermore, inspired by the observation that different views share similar data structures, a constraint is introduced to enforce the view-consistency of the multiple linear transforms. The proposed method is evaluated on three tasks: face recognition across pose, photo versus. sketch face recognition, and visual light image versus near infrared image face recognition on Multi-PIE, CUFSF and HFB databases respectively. Extensive experiments show that our MvDA achieves significant improvements compared with the best known results.
关键词	Multi-view discriminant analysis cross-view recognition heterogeneous recognition common space
DOI	10.1109/TPAMI.2015.2435740
收录类别	SCI
语种	英语
资助项目	973 Program[2015CB351802] ; Natural Science Foundation of China[61173065] ; Natural Science Foundation of China[61222211] ; Natural Science Foundation of China[61402443] ; Natural Science Foundation of China[61390511] ; R&D Program for Implementation of Anti-Crime and Anti-Terrorism Technologies for a Safe and Secure Society, Special Coordination Fund for Promoting Science and Technology of MEXT, the Japanese Government
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000366669200014
出版者	IEEE COMPUTER SOC
引用统计	被引频次：441[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/9042
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Kan, Meina
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Omron Social Solut Co LTD, Kyoto, Japan
推荐引用方式 GB/T 7714	Kan, Meina,Shan, Shiguang,Zhang, Haihong,et al. Multi-View Discriminant Analysis[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2016,38(1):188-194.
APA	Kan, Meina,Shan, Shiguang,Zhang, Haihong,Lao, Shihong,&Chen, Xilin.(2016).Multi-View Discriminant Analysis.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,38(1),188-194.
MLA	Kan, Meina,et al."Multi-View Discriminant Analysis".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 38.1(2016):188-194.