Sparse Nonnegative Matrix Factorization using ℓ0-Constraints

TitleSparse Nonnegative Matrix Factorization using ℓ0-Constraints
Publication TypeConference Paper
Year of Publication2010
AuthorsPeharz, R., Stark M., & Pernkopf F.
Conference NameProceedings of MLSP
Pages83 - 88
Date PublishedAug
ISBN Number978-1-4244-7875-0

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 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 and nonnegative KSVD.

Citation KeyID52513
Full Text

See also the more recent article in Neuromputing ( and in the publication area.

peharz2010.pdf365.6 KB