PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Uniqueness of non-Gaussianity-based dimension reduction
Fabian Theis, Motoaki Kawanabe and Klaus-Robert Müller
IEEE Transactions on Signal Processing Volume 59, Number 9, pp. 4478-4482, 2011.

Abstract

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:9417
Deposited By:Klaus-Robert Müller
Deposited On:16 March 2012