Comment on "Blind source separation based on endpoint estimation with applications to the MLSP 2006 data competition"

Sergio Cruces, Andrzej Cichocki

Research output: Contribution to journalComment/debate

Abstract

This note illustrates some shortcomings of the criterion proposed in "Blind source separation based on endpoint estimation with applications to the MLSP 2006 data competition" when the number of samples is finite. This algorithm considers mutually independent sources with semibounded support, however, even for a sufficient sample size for which the finite bound of the support of the density of the output can be estimated accurately, the endpoint estimate nearest to the mean might be in the unbounded side of this density. In that case, the superadditivity of the least absolute endpoint estimate is usually violated, causing the loss of the contrast function property and of the capability of discriminating hidden sources, for practical versions of this criterion.

Original languageEnglish
Pages (from-to)863-865
Number of pages3
JournalNeurocomputing
Volume74
Issue number5
DOIs
Publication statusPublished - Feb 2011
Externally publishedYes

Keywords

  • Blind source separation
  • Independent component analysis
  • Least absolute endpoint

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