Abstract
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. As the proposed target function has many local minima, we use a genetic algorithm for its minimization. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Stadlthanner, K., Theis, F. J., Lang, E. W., Tomé, A. M., Puntonet, C. G., Vilda, P. G., … Schmitz, G. (2006). Sparse nonnegative matrix factorization applied to microarray data sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 254–261). https://doi.org/10.1007/11679363_32
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
