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• W2087613098 abstract "Non-negative Matrix Factorisation (NMF) is a popular tool in which a `parts-based' representation of a non-negative matrix is sought. NMF tends to produce sparse decompositions. This sparsity is a desirable property in many applications, and Sparse NMF (S-NMF) methods have been proposed to enhance this feature. Typically these enforce sparsity through use of a penalty term, and a ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> norm penalty term is often used. However an ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> penalty term may not be appropriate in a non-negative framework. In this paper the use of a ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> norm penalty for NMF is proposed, approximated using backwards elimination from an initial NNLS decomposition. Dictionary recovery experiments using overcomplete dictionaries show that this method outperforms both NMF and a state of the art S-NMF method, in particular when the dictionary to be learnt is dense." @default.
• W2087613098 created "2016-06-24" @default.
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• W2087613098 date "2013-12-01" @default.
• W2087613098 modified "2023-09-25" @default.
• W2087613098 title "Learning overcomplete dictionaries with &#x2113;<inf>0</inf>-sparse Non-negative Matrix Factorisation" @default.
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• W2087613098 doi "https://doi.org/10.1109/globalsip.2013.6737056" @default.
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