On the computation of a nonnegative matrix factorization and its application in telecommunications,



The Nonnegative Matrix Factorization (NMF) has become an increasingly popular approach in many areas of
telecommunications. A number of properties and a nonlinear programming formulation for NMF are introduced, which allow approximations to the solution of diverse image processing problems, ranging from data analysis to video summarization, pattern recognition and image reconstruction. A spectral projected-gradient algorithm is investigated for the solution of the corresponding optimization problem. Techniques for finding an initial point and dealing with the sparsity of the decomposition are also discussed. Some computational experience is reported to highlight the efficacy of these techniques in practice.


Combinatorial Optimization, image processing, feature extraction, dimensinality reduction


Feature extraction; PG Methods


Conftele 2009, The 7th Conference on Telecommunications, May 2009

Cited by

Year 2009 : 1 citations

 ”Knowledge extraction with non-negative matrix factorization for text classification”, C.
Silva e B. Ribeiro, Proc Iof the 10th international conference on Intelligent data engineering
and automated learning , pp. 300–3086, Springer-Verlag Berlin, Heidelberg, 2009