Extended sparse nonnegative matrix factorization

Authors:
Kurt Stadlthanner;Fabian J. Theis;Carlos G. Puntonet;Elmar W. Lang
Affiliations:
Institute of Biophysics, University of Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany;Dept. Arq. y Téc. de Comp., Universidad de Granada, Granada, Spain;Institute of Biophysics, University of Regensburg, Regensburg, Germany
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 1
Cited 2

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Abstract

In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended sparse NMF algorithm for blind source separation is investigated.