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Sparse ICA via cluster-wise PCA

Babaie Zadeh, M ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1016/j.neucom.2005.12.022
  3. Publisher: 2006
  4. Abstract:
  5. 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. © 2006 Elsevier B.V. All rights reserved
  6. Keywords:
  7. Blind source separation ; Principal component analysis ; Signal processing ; Clustering algorithm ; Sparse signal ; Independent component analysis ; Algorithm ; Cluster analysis ; Geometry ; Independent component analysis ; Mathematical analysis ; Mathematical computing ; Principal component analysis ; Priority journal ; Reliability ; Sampling
  8. Source: Neurocomputing ; Volume 69, Issue 13-15 , 2006 , Pages 1458-1466 ; 09252312 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0925231205003383