Sparse nonnegative matrix factorization using ℓ0-constraints

Robert Peharz, Michael Stark, Franz Pernkopf

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

16 Citations (Scopus)

Abstract

Although nonnegative matrix factorization (NMF) favors a part-based and sparse representation of its input, there is no guarantee for this behavior. Several extensions to NMF have been proposed in order to introduce sparseness via the ℓ1-norm, while little work is done using the more natural sparseness measure, the ℓ0-pseudo-norm. In this work we propose two NMF algorithms with ℓ0-sparseness constraints on the bases and the coefficient matrices, respectively. We show that classic NMF [1] is a suited tool for ℓ0-sparse NMF algorithms, due to a property we call sparseness maintenance. We apply our algorithms to synthetic and real-world data and compare our results to sparse NMF [2] and nonnegative KSVD [3].

Original languageEnglish
Title of host publicationProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
Pages83-88
Number of pages6
DOIs
Publication statusPublished - 24 Nov 2010
Externally publishedYes
Event2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 - Kittila, Finland
Duration: 29 Aug 20101 Sep 2010

Conference

Conference2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
CountryFinland
CityKittila
Period29/08/101/09/10

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