• DocumentCode
    1188191
  • Title

    Local minima of information-theoretic criteria in blind source separation

  • Author

    Pham, Dinh-Tuan ; Vrins, Frédé

  • Author_Institution
    UCL Machine Learning Group, Univ. Catholique de Louvain, Louvain-la-Neuve, Belgium
  • Volume
    12
  • Issue
    11
  • fYear
    2005
  • Firstpage
    788
  • Lastpage
    791
  • Abstract
    Recent simulation results have indicated that spurious minima in information-theoretic criteria with an orthogonality constraint for blind source separation may exist. Nevertheless, those results involve approximations (e.g., density estimation), so that they do not constitute an absolute proof. In this letter, the problem is tackled from a theoretical point of view. An example is provided for which it is rigorously proved that spurious minima can exist in both mutual information and negentropy optima. The proof is based on a Taylor expansion of the entropy.
  • Keywords
    blind source separation; independent component analysis; minimisation; minimum entropy methods; BSS; Taylor expansion; blind source separation; independent component analysis; minimisation; mutual information-theoretic criteria; negentropy; Blind source separation; Cost function; Data mining; Entropy; Independent component analysis; Iterative algorithms; Mutual information; Source separation; Taylor series; Vectors; Blind source separation (BSS); entropy; independent component analysis; mutual information;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2005.856868
  • Filename
    1518902