Uniqueness of Non-Gaussianity-Based Dimension Reduction

  title={Uniqueness of Non-Gaussianity-Based Dimension Reduction},
  author={Fabian J. Theis and Motoaki Kawanabe and Klaus-Robert M{\"u}ller},
  journal={IEEE Transactions on Signal Processing},
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… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 16 references

Colored Subspace Analysis: Dimension Reduction Based on a Signal's Autocorrelation Structure

IEEE Transactions on Circuits and Systems I: Regular Papers • 2010
View 5 Excerpts

Hyperspectral Subspace Identification

IEEE Transactions on Geoscience and Remote Sensing • 2008

Minimum description length

Scholarpedia • 2008
View 1 Excerpt

A new algorithm of non-Gaussian component analysis with radial kernel functions

M. Kawanabe, M. Sugiyama, G. Blanchard, K.-R. Müller
Ann. Inst. Statist. Math., vol. 59, no. 1, pp. 57–75, 2007. • 2007
View 1 Excerpt

Similar Papers

Loading similar papers…