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  • Research Article
  • Open Access

Geometrical Interpretation of the PCA Subspace Approach for Overdetermined Blind Source Separation

EURASIP Journal on Advances in Signal Processing20062006:071632

  • Received: 25 January 2005
  • Accepted: 26 August 2005
  • Published:


We discuss approaches for blind source separation where we can use more sensors than sources to obtain a better performance. The discussion focuses mainly on reducing the dimensions of mixed signals before applying independent component analysis. We compare two previously proposed methods. The first is based on principal component analysis, where noise reduction is achieved. The second is based on geometric considerations and selects a subset of sensors in accordance with the fact that a low frequency prefers a wide spacing, and a high frequency prefers a narrow spacing. We found that the PCA-based method behaves similarly to the geometry-based method for low frequencies in the way that it emphasizes the outer sensors and yields superior results for high frequencies. These results provide a better understanding of the former method.


  • Principal Component Analysis
  • Information Technology
  • Quantum Information
  • Independent Component
  • Noise Reduction

Authors’ Affiliations

Department of Multimedia Communication and Signal Processing, University of Erlangen-Nuremberg, Erlangen, 91058, Germany
NTT Communication Science Laboratories, NTT Corporation, 2–4 Hikaridai Seika-cho, Soraku-gun Kyoto, 619-0237, Japan


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© Winter et al. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.