Extended sparse nonnegative matrix factorization

Kurt Stadlthanner, Fabian J. Theis, Carlos G. Puntonet, Elmar W. Lang

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


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.

Original languageEnglish
Pages (from-to)249-256
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2005
Externally publishedYes
Event8th International Workshop on Artificial Neural Networks, IWANN 2005: Computational Intelligence and Bioinspired Systems - Vilanova i la Geltru, Spain
Duration: 8 Jun 200510 Jun 2005


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