Geometrical interpretation of the PCA subspace approach for overdetermined blind source separation

Journal article


Publication Details

Author(s): Winter S, Sawada H, Makino S
Journal: EURASIP J APPL SIG P
Publication year: 2006
Volume: 2006
ISSN: 1110-8657
Language: English


Abstract

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.


How to cite

APA:
Winter, S., Sawada, H., & Makino, S. (2006). Geometrical interpretation of the PCA subspace approach for overdetermined blind source separation. EURASIP J APPL SIG P, 2006. https://dx.doi.org/10.1155/ASP/2006/71632

MLA:
Winter, Stefan, H. Sawada, and Shoji Makino. "Geometrical interpretation of the PCA subspace approach for overdetermined blind source separation." EURASIP J APPL SIG P 2006 (2006).

BibTeX: 

Last updated on 2019-29-05 at 21:10