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An EM algorithm for learning sparse and overcomplete representations
Zhong, MJ; Tang, HW; Chen, HJ; Tang, YY
2004-03-01
Source PublicationNEUROCOMPUTING
ISSN0925-2312
SubtypeArticle
Volume57Pages:469-476
AbstractAn expectation-maximization (EM) algorithm for learning sparse and overcomplete representations is presented in this paper. We show that the estimation of the conditional moments of the posterior distribution can be accomplished by maximum a posteriori estimation. The approximate conditional moments,enable the development of an EM algorithm for learning the overcomplete basis vectors and inferring the most probable basis coefficients. (C) 2003 Elsevier B.V. All rights reserved.
Keywordovercomplete representations EM algorithm maximum a posteriori
Indexed BySCI
Language英语
WOS IDWOS:000220670700027
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/14017
Collection中国科学院心理研究所回溯数据库(1956-2010)
Affiliation1.Dalian Univ Technol, Inst Neuroinformat, Dept Foreign Languages, Dalian 116023, Peoples R China
2.Dalian Univ Technol, Inst Computat Biol & Bioinformat, Dalian 116023, Peoples R China
3.Chinese Acad Sci, Lab Visual Informat Proc, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Key Lab Mental Hlth, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Zhong, MJ,Tang, HW,Chen, HJ,et al. An EM algorithm for learning sparse and overcomplete representations[J]. NEUROCOMPUTING,2004,57:469-476.
APA Zhong, MJ,Tang, HW,Chen, HJ,&Tang, YY.(2004).An EM algorithm for learning sparse and overcomplete representations.NEUROCOMPUTING,57,469-476.
MLA Zhong, MJ,et al."An EM algorithm for learning sparse and overcomplete representations".NEUROCOMPUTING 57(2004):469-476.
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