Independent component analysis with feature selective filtering

Yi Ou Li, Tülay Adali, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this contribution, we propose a feature selective filtering scheme for independent component analysis (ICA) to improve the estimation of the sources of interest (SOI), i.e., sources that have certain desired features in their sample space. As an example, we show that ICA with a smooth filtering scheme can improve the estimation of the smooth image sources from a mixture of images, as well as the estimation of a smooth visual activation map in a hybrid functional magnetic resonance imaging (fMRI) data set. Hence, the technique can potentially be used in the analysis of fMRI data to improve the ICA estimation of functional activation regions that are expected to be smooth.

Original languageEnglish (US)
Title of host publicationMachine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop
EditorsA. Barros, J. Principe, J. Larsen, T. Adali, S. Douglas
Pages193-202
Number of pages10
StatePublished - Dec 1 2004
Externally publishedYes
EventMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis, Brazil
Duration: Sep 29 2004Oct 1 2004

Publication series

NameMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop

Other

OtherMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
CountryBrazil
CitySao Luis
Period9/29/0410/1/04

ASJC Scopus subject areas

  • Engineering(all)

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