Sparse ICA via cluster-wise PCA

NEUROCOMPUTING

卷:69

Article; Proceedings Paper

Babaie-Zadeh, Massoud ; Jutten, Christian ; Mansour, Ali

关键词: independent component analysis (ICA);    blind source seperation (BSS);    sparse ICA;    principal component analysis (PCA);   

DOI  :  10.1016/j.neucom.2005.12.022

来源: Elsevier

【 摘 要 】

In this paper, it is shown that independent component analysis (ICA) of sparse signals (sparse ICA) can be seen as a cluster-wise principal component analysis (PCA). Consequently, Sparse ICA may be done by a combination of a clustering algorithm and PCA. For the clustering part, we use, in this paper, an algorithm inspired from K-means. The final algorithm is easy to implement for any number of sources. Experimental results points out the good performance of the method, whose the main restriction is to request an exponential growing of the sample number as the number of sources increases. (c) 2006 Elsevier B.V. All rights reserved.

【 授权许可】

Free