Go to content
UR Home

Uniqueness of non-gaussian subspace analysis

Theis, Fabian J. and Kawanabe, M. (2006) Uniqueness of non-gaussian subspace analysis. In: Rosca, J., (ed.) Independent Component Analysis and Blind Signal Separation, 6th International Conference, ICA 2006, Charleston, SC, USA, March 5-8, 2006. Proceedings. Lecture notes in computer science, 3889. Springer, Berlin, pp. 917-925. ISBN 3-540-32630-8 (print), 978-3-540-32630-4 (e-book).

Full text not available from this repository.

at publisher (via DOI)


Dimension reduction provides an important tool for preprocessing large scale data sets. A possible model for dimension reduction is realized by projecting onto the non-Gaussian part of a given multivariate recording. We prove that the subspaces of such a projection are unique given that the Gaussian subspace is of maximal dimension. This result therefore guarantees that projection algorithms uniquely recover the underlying lower dimensional data signals.

Export bibliographical data

Item type:Book section
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
Related URLs:
Dewey Decimal Classification:500 Science > 570 Life sciences
Created at the University of Regensburg:Unknown
Item ID:16868
Owner only: item control page
  1. Homepage UR

University Library

Publication Server


Publishing: oa@ur.de

Dissertations: dissertationen@ur.de

Research data: daten@ur.de

Contact persons