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Inlier-based ICA with an application to superimposed images




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).

Author(s): Meinecke, F. and Harmeling, S. and Müller, K-R.
Journal: International Journal of Imaging Systems and Technology
Volume: 15
Number (issue): 1
Pages: 48-55
Year: 2005
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1002/ima.20037
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Inlier-based ICA with an application to superimposed images},
  author = {Meinecke, F. and Harmeling, S. and M{\"u}ller, K-R.},
  journal = {International Journal of Imaging Systems and Technology},
  volume = {15},
  number = {1},
  pages = {48-55},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2005},
  month_numeric = {7}