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A comparative study and improvement of two ICA using reference signal methods

  • Jian Xun Mi*
  • , Yong Xu
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present a comparative assessment of two ICA using reference signal methods. Independent Component Analysis (ICA) using reference signal is a useful tool for extracting a desired independent component (IC). Reference signal is served as a priori information to conduct the one-unit ICA to converge to the local extreme point related to a desired IC. There are two methods can perform ICA using reference signal, namely ICA with reference (ICA-R) and fast ICA with reference signal (FICAR). This paper intends to fill in a gap in the previous studies to give comparisons of those methods systematically. Moreover, we also provide useful improvements to the two methods. Firstly, we propose a improved algorithm to fix the flaw in the previous FICAR. Secondly, for ICA-R, we propose a criterion to select appropriate distance measurement.

Original languageEnglish
Pages (from-to)157-164
Number of pages8
JournalNeurocomputing
Volume137
DOIs
StatePublished - 5 Aug 2014
Externally publishedYes

Keywords

  • ICA with reference signal
  • Independent component analysis
  • Wiener filter

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