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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2018

External authors: Grazia Lotti (Università di Parma), Ornella Menchi (Università di Pisa), Francesco Romani (Università di Pisa)

IIT authors:

Paola Favati

Type: Rapporto Tecnico
Field of reference: Mathematics
IIT TR-05/2018

File: IIT-05-2018.pdf

Activity: Metodi numerici per problemi di grandi dimensioni