CUED Publications database

Sparse nonnegative matrix factorization using ℓ<sup>0</sup>-constraints

Peharz, R and Stark, M and Pernkopf, F (2010) Sparse nonnegative matrix factorization using ℓ<sup>0</sup>-constraints. In: UNSPECIFIED pp. 83-88..

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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 ℓ -norm, while little work is done using the more natural sparseness measure, the ℓ -pseudo-norm. In this work we propose two NMF algorithms with ℓ -sparseness constraints on the bases and the coefficient matrices, respectively. We show that classic NMF [1] is a suited tool for ℓ -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]. ©2010 IEEE. 1 0 0 0

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 18 Oct 2017 20:08
Last Modified: 08 Apr 2021 06:08
DOI: 10.1109/MLSP.2010.5589219