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A fast fixed-point algorithm for complexity pursuit
Shi, ZW; Tang, HW; Tang, YY
2005-03-01
Source PublicationNEUROCOMPUTING
ISSN0925-2312
SubtypeArticle
Volume64Pages:529-536
AbstractComplexity pursuit is a recently developed algorithm using the gradient descent for separating interesting components from time series. It is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and independent component analysis (ICA). In this paper, a fixed-point algorithm for complexity pursuit is introduced. The fixed-point algorithm inherits the advantages of the well-known FastICA algorithm in ICA, which is very simple, converges fast, and does not need choose any learning step sizes. (c) 2005 Elsevier B.V. All rights reserved.
Keywordindependent component analysis blind source separation complexity pursuit projection pursuit time series
Indexed BySCI
Language英语
WOS IDWOS:000227922700031
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/14070
Collection中国科学院心理研究所回溯数据库(1956-2010)
Affiliation1.Dalian Univ Technol, Inst Computat Biol & Bioinformat, Dalian 116023, Peoples R China
2.Dalian Univ Technol, Inst Neuroinformat, 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
Shi, ZW,Tang, HW,Tang, YY. A fast fixed-point algorithm for complexity pursuit[J]. NEUROCOMPUTING,2005,64:529-536.
APA Shi, ZW,Tang, HW,&Tang, YY.(2005).A fast fixed-point algorithm for complexity pursuit.NEUROCOMPUTING,64,529-536.
MLA Shi, ZW,et al."A fast fixed-point algorithm for complexity pursuit".NEUROCOMPUTING 64(2005):529-536.
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