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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).

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Abstract

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.


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Item Type:Book Section
Date:2006
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
10.1007/11679363_114DOI
Related URLs:
URLURL Type
http://www.springerlink.com/content/t41570185123m40u/Publisher
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Deposited On:01 Oct 2010 08:00
Last Modified:01 Oct 2010 08:00
Item ID:16868
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