Go to content
UR Home

Sparse Nonnegative Matrix Factorization Applied to Microarray Data Sets

Stadlthanner, K., Theis, Fabian J., Lang, Elmar, Tomé, A., Puntonet, C. G., Gomez-Vilda, P., Langmann, T. and Schmitz, G. (2006) Sparse Nonnegative Matrix Factorization Applied to Microarray Data Sets. 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. 254-261. ISBN 3-540-32630-8 (print), 978-3-540-32630-4 (e-book).

Full text not available from this repository.

at publisher (via DOI)


Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. However, it is known not to lead to unique results when applied to nonnegative Blind Source Separation (BSS) problems. In this paper we present first results of an extension to the NMF algorithm which solves the BSS problem when the underlying sources are sufficiently sparse. ...


Export bibliographical data

Item type:Book section
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
Dewey Decimal Classification:500 Science > 570 Life sciences
Created at the University of Regensburg:Unknown
Item ID:16867
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