On an adaptive ICA method with application to biomedical image analysis

B. M. Hong, V. D. Calhoun

Research output: Contribution to conferencePaper

Abstract

Conventional ICA algorithms typically model the probability density functions of the underlying sources as highly kurtotic or symmetric. However, when source data violate the assumptions (e.g., low kurtosis), the conventional ICA methods might not work well. Adaptive modeling of the underlying sources thus becomes an important issue for ICA applications. This paper proposes the Log Weibull model to represent skewed distributed sources within the Infomax framework and further introduces an adaptive ICA method. The central idea is to use a two-stage separation process: 1) Conventional ICA used for all channel sources to obtain initial independent source estimates; 2) source density estimate-based nonlinearities adaptively used for the "refitting" separation to all channel sources. The ICA algorithm is based on flexible nonlinearities of density matched candidates. Our simulations demonstrate the effectiveness of this approach.

Original languageEnglish (US)
Pages2245-2248
Number of pages4
StatePublished - Nov 16 2004
Externally publishedYes
Event2004 7th International Conference on Signal Processing Proceedings (ICSP'04) - Beijing, China
Duration: Aug 31 2004Sep 4 2004

Other

Other2004 7th International Conference on Signal Processing Proceedings (ICSP'04)
CountryChina
CityBeijing
Period8/31/049/4/04

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Science Applications

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  • Cite this

    Hong, B. M., & Calhoun, V. D. (2004). On an adaptive ICA method with application to biomedical image analysis. 2245-2248. Paper presented at 2004 7th International Conference on Signal Processing Proceedings (ICSP'04), Beijing, China.