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A clustering approach for blind source separation with more sources than mixtures
Shi, ZW; Tang, HW; Tang, YY; Yin, FL; Wang, J; Guo, CG
2004
Source PublicationADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1
ISSN0302-9743
SubtypeArticle
Volume3173Pages:684-689
AbstractIn this paper, blind source separation is discussed with more sources than mixtures when the sources are sparse. The blind separation technique includes two steps. The first step is to estimate a mixing matrix, and the second is to estimate sources. The mixing matrix can be estimated by using a clustering approach which is described by the generalized exponential mixture model. The generalized exponential mixture model is a powerful uniform framework to learn the mixing matrix for sparse sources. After the mixing matrix is estimated, the sources can be obtained by solving a linear programming problem. The techniques we present here can be extended to the blind separation of more sources than mixtures with a Gaussian noise.
Indexed ByISTP ; SCI
Language英语
WOS IDWOS:000223492600112
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/13905
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,et al. A clustering approach for blind source separation with more sources than mixtures[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1,2004,3173:684-689.
APA Shi, ZW,Tang, HW,Tang, YY,Yin, FL,Wang, J,&Guo, CG.(2004).A clustering approach for blind source separation with more sources than mixtures.ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1,3173,684-689.
MLA Shi, ZW,et al."A clustering approach for blind source separation with more sources than mixtures".ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1 3173(2004):684-689.
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