Robust independent component analysis via time-delayed cumulant functions

Pando Georgiev, Andrzej Chichocki

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

In this paper we consider blind source separation (BSS) problem of signals with are spatially uncorrelated of order four, but temporally correlated of order four (for instance speech or biomedical signals). For such type of signals we propose a new sufficient condition for separation using fourth order statistics, stating that the separation is possible, if the source signals have distinct normalized cumulant functions (depending on time delay). Using this condition we show that the BSS problem can be converted to a symmetric eigenvalue problem of a generalized cumulant matrix Z(4) (b) depending on L-dimensional parameter b, if this matrix has distinct eigenvalues. We prove that the set of parameters b which produce Z(4) (b) with distinct eigenvalues form an open subset of RL, whose complement has a measure zero. We propose a new separating algorithm which uses Jacobi's method for joint diagonalization of cumulant matrices depending on time delay. We emphasize the following two features of this algorithm: 1) The optimal number of matrices for joint diago- nalization is 100-150 (established experimentally), which for large dimensional problems is much smaller than those of JADE; 2) It works well even if the signals from the above class are, additionally, white (of order two) with zero kurtosis (as shown by an example).

Original languageEnglish
Pages (from-to)573-579
Number of pages7
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE86-A
Issue number3
Publication statusPublished - Mar 2003
Externally publishedYes

Keywords

  • Blind source separation
  • Cumulant functions
  • Eigenvalue decomposition
  • Independent component analysis
  • Join diagonalization

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