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arXiv · 1903.01321

Adaptive computation of the Symmetric Nonnegative Matrix Factorization (NMF)

Paola Favati·Grazia Lotti·Ornella Menchi·Francesco Romani
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Abstract

Nonnegative Matrix Factorization (NMF), first proposed in 1994 for data analysis, has received successively much attention in a great variety of contexts such as data mining, text clustering, computer vision, bioinformatics, etc. In this paper the case of a symmetric matrix is considered and the symmetric nonnegative matrix factorization (SymNMF) is obtained by using a penalized nonsymmetric minimization problem. Instead of letting the penalizing parameter increase according to an a priori fixed rule, as suggested in literature, we propose a heuristic approach based on an adaptive technique. Extensive experimentation shows that the proposed algorithm is effective.

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Paola Favati, Grazia Lotti, Ornella Menchi, Francesco Romani. 2019-03-04. Adaptive computation of the Symmetric Nonnegative Matrix Factorization (NMF). https://arxiv.org/abs/1903.01321

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