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SHR Neuro Cancer Cardio Lipid

Peharz, R; Pernkopf, F.

Neurocomputing. 2012; 80(1):38-46 [OPEN ACCESS]
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Authors Med Uni Graz:
Peharz Robert
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Number of Figures: 8
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Abstract:
Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of nonnegative data, there is no guarantee for this behavior. Several authors proposed NMF methods which enforce sparseness by constraining or penalizing the [Formula: see text] of the factor matrices. On the other hand, little work has been done using a more natural sparseness measure, the [Formula: see text]. In this paper, we propose a framework for approximate NMF which constrains the [Formula: see text] of the basis matrix, or the coefficient matrix, respectively. For this purpose, techniques for unconstrained NMF can be easily incorporated, such as multiplicative update rules, or the alternating nonnegative least-squares scheme. In experiments we demonstrate the benefits of our methods, which compare to, or outperform existing approaches.

Find related publications in this database (Keywords)
NMF
Sparse coding
Nonnegative least squares
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