Inlier-based ICA with an application to superimposed images

Frank C. Meinecke, Stefan Harmeling, Klaus Muller

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlier-based ICA (IBICA) is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals).

Original languageEnglish
Pages (from-to)48-55
Number of pages8
JournalInternational Journal of Imaging Systems and Technology
Volume15
Issue number1
DOIs
Publication statusPublished - 2005 Oct 5
Externally publishedYes

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Keywords

  • Blind Source Separation (BSS)
  • Independent Component Analysis (ICA)
  • Outlier robustness
  • Overcomplete ICA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

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