Uniqueness of non-gaussianity-based dimension reduction

Fabian J. Theis, Motoaki Kawanabe, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Dimension reduction is a key step in preprocessing large-scale data sets. A recently proposed method named non-Gaussian component analysis searches for a projection onto the non-Gaussian part of a given multivariate recording, which is a generalization of the deflationary projection pursuit model. In this contribution, we discuss the uniqueness of the subspaces of such a projection. We prove that a necessary and sufficient condition for uniqueness is that the non-Gaussian signal subspace is of minimal dimension. Furthermore, we propose a measure for estimating this minimal dimension and illustrate it by numerical simulations. Our result guarantees that projection algorithms uniquely recover the underlying lower dimensional data signals.

Original languageEnglish
Article number5876340
Pages (from-to)4478-4482
Number of pages5
JournalIEEE Transactions on Signal Processing
Issue number9
Publication statusPublished - 2011 Sept


  • Identifiability
  • independent subspace analysis
  • non-Gaussian component analysis
  • projection pursuit

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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