On connections between Renyi entropy Principal Component Analysis, kernel learning and graph embedding

doi:10.1016/j.patrec.2018.06.011

CASIA OpenIR > 模式识别国家重点实验室

	On connections between Renyi entropy Principal Component Analysis, kernel learning and graph embedding
	Ran, Zhi-Yong 1; Wang, Wei 2; Hu, Bao-Gang3
发表期刊	PATTERN RECOGNITION LETTERS
ISSN	0167-8655
	2018-09-01
卷号	112 页码:125-130
文章类型	Article
摘要	In this paper, we study the connections between Renyi entropy PCA, kernel learning and graph embedding. A natural complementary formulation of maximum entropy PCA, namely minimum error entropy PCA, is presented. These two formulations can be combined together to give a two-fold understanding of Renyi entropy PCA. Further, we establish connections between Renyi entropy PCA, kernel learning and graph embedding, and propose a generalized graph embedding framework that unifies a variety of existing algorithms. This proposed framework essentially covers previous graph embedding framework, and partially answers the problem of how to make use of high order statistics of data in dimensionality reduction. The theoretic development enables a close relationship between information theoretic learning, kernel learning and graph embedding. (c) 2018 Elsevier B.V. All rights reserved.
关键词	Renyi Entropy Pca Kernel Learning Graph Embedding
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.patrec.2018.06.011
关键词[WOS]	NONLINEAR DIMENSIONALITY REDUCTION ; FRAMEWORK
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61502466 ; 61503365)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000443950800018
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27917
专题	模式识别国家重点实验室
通讯作者	Wang, Wei
作者单位	1.Chongqing Univ Posts & Telecommun, Chongqing 400065, Peoples R China 2.Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ran, Zhi-Yong,Wang, Wei,Hu, Bao-Gang. On connections between Renyi entropy Principal Component Analysis, kernel learning and graph embedding[J]. PATTERN RECOGNITION LETTERS,2018,112:125-130.
APA	Ran, Zhi-Yong,Wang, Wei,&Hu, Bao-Gang.(2018).On connections between Renyi entropy Principal Component Analysis, kernel learning and graph embedding.PATTERN RECOGNITION LETTERS,112,125-130.
MLA	Ran, Zhi-Yong,et al."On connections between Renyi entropy Principal Component Analysis, kernel learning and graph embedding".PATTERN RECOGNITION LETTERS 112(2018):125-130.