Convex nonnegative matrix factorization with missing data - Aix-Marseille Université Access content directly
Conference Papers Year : 2016

Convex nonnegative matrix factorization with missing data


Convex nonnegative matrix factorization (CNMF) is a variant of nonnegative matrix factorization (NMF) in which the components are a convex combination of atoms of a known dictionary. In this contribution, we propose to extend CNMF to the case where the data matrix and the dictionary have missing entries. After a formulation of the problem in this context of missing data, we propose a majorization-minimization algorithm for the solving of the optimization problem incurred. Experimental results with synthetic data and audio spectrograms highlight an improvement of the performance of reconstruction with respect to standard NMF. The performance gap is particularly significant when the task of reconstruction becomes arduous, e.g. when the ratio of missing data is high, the noise is steep, or the complexity of data is high.
Fichier principal
Vignette du fichier
mlsp2016.pdf (623.3 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-01346492 , version 1 (07-10-2016)


  • HAL Id : hal-01346492 , version 1


Ronan Hamon, Valentin Emiya, Cédric Févotte. Convex nonnegative matrix factorization with missing data. IEEE International Workshop on Machine Learning for Signal Processing, Sep 2016, Vietri sul Mare, Salerno, Italy. ⟨hal-01346492⟩
531 View
1295 Download


Gmail Facebook X LinkedIn More