Sparse ICA via cluster-wise PCA

Authors:
Massoud Babaie-Zadeh;Christian Jutten;Ali Mansour
Affiliations:
Advanced Communications Research Institute (ACRI) and Electrical Engineering Department, Sharif University of Technology, Tehran, Iran;Laboratory of Images and Signals (CNRS UMR 5083, INPG, UJF), Grenoble, France;E3I2, ENSIETA, Brest, France
Venue:
Neurocomputing
Year:
2006

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Abstract

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.