Geometrical interpretation of the PCA subspace approach for overdetermined blind source separation
type: Item
instance of: scholarly article
title: Geometrical interpretation of the PCA subspace approach for overdetermined blind source separation
zbMATH Open document ID: 1122.94316
DOI: 10.1155/ASP/2006/71632
author: N. E. Zubov
published in: EURASIP Journal on Applied Signal Processing
publication date: 2007-03-19T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 5134694
Wikidata QID: Q59212774
MaRDI profile type: Publication
cites work: Topographic independent component analysis
description: scientific article; zbMATH DE number 5134694