NDLI: Geometrical understanding of the PCA subspace method for overdetermined blind source separation

Content Provider	IEEE Xplore Digital Library
Author	Winter, S. Sawada, H. Makino, S.
Copyright Year	2003
Description	Author affiliation: Commun. Sci. Labs., NTT Corp., Kyoto, Japan (Winter, S.; Sawada, H.; Makino, S.)
Abstract	We discuss approaches for blind source separation where we can use more sensors than the number of sources for a better performance. The discussion focuses mainly on reducing the dimension 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 involves selecting a subset of sensors based on 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, which provides a better understanding of the former method.
Sponsorship	IEEE Signal Process, Soc
File Size	300017
File Format	PDF
ISBN	0780376633
ISSN	15206149
DOI	10.1109/ICASSP.2003.1202480
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-04-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Blind source separation Independent component analysis Frequency Source separation Sensor systems Time domain analysis Discrete Fourier transforms Laboratories Noise reduction
Content Type	Text
Resource Type	Article

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