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CORR
2004
Springer

Non-negative matrix factorization with sparseness constraints

9 years 1 months ago
Non-negative matrix factorization with sparseness constraints
Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of `sparseness' improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data-analysis problems.
Patrik O. Hoyer
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2004
Where CORR
Authors Patrik O. Hoyer
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