Quadratic independent component analysis

Fabian J. Theis, Wakako Nakamura

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear parametrization of the monomials, which can also be applied for higher-order polynomials. The paper finishes with simulations for artificial data and natural images.

Original languageEnglish
Pages (from-to)2355-2363
Number of pages9
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE87-A
Issue number9
StatePublished - Sep 2004
Externally publishedYes

Keywords

  • Natural images
  • Nonlinear blind source separation
  • Nonlinear independent component analysis
  • Overdetermined blind source separation
  • Quadratic forms

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