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- W2043252714 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 ℓ <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> -norm, while little work is done using the more natural sparseness measure, the ℓ <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sup> -pseudo-norm. In this work we propose two NMF algorithms with ℓ <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sup> -sparseness constraints on the bases and the coefficient matrices, respectively. We show that classic NMF is a suited tool for ℓ <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sup> -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 K-SVD." @default.
- W2043252714 created "2016-06-24" @default.
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- W2043252714 date "2010-08-01" @default.
- W2043252714 modified "2023-09-24" @default.
- W2043252714 title "Sparse nonnegative matrix factorization using ℓ<sup>0</sup>-constraints" @default.
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- W2043252714 doi "https://doi.org/10.1109/mlsp.2010.5589219" @default.
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