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Inlier‐based ICA with an application to superimposed images
Author(s) -
Meinecke Frank C.,
Harmeling Stefan,
Müller KlausRobert
Publication year - 2005
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20037
Subject(s) - outlier , independent component analysis , pattern recognition (psychology) , artificial intelligence , computer science , gaussian , anomaly detection , feature (linguistics) , linguistics , philosophy , physics , quantum mechanics
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). © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 48–55, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20037