Non-Negative Tensor Dictionary Learning

Abraham Traoré (1) , Maxime Berar (2) , Alain Rakotomamonjy (2)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 DocApp - LITIS - Equipe Apprentissage

A challenge faced by dictionary learning and non-negative matrix factorization is to efficiently model, in a context of feature learning, temporal patterns for data presenting sequential (two-dimensional) structure such as spectrograms. In this paper, we address this issue through tensor factorization. For this purpose, we make clear the connection between dictionary learning and tensor factorization when several examples are available. From this connection, we derive a novel (supervised) learning problem which induces emergence of temporal patterns in the learned dictionary. Obtained features are compared in a classification framework with those obtained by NMF and achieve promising results.

Domaines: Intelligence artificielle [cs.AI]

Abraham Traoré, Maxime Berar, Alain Rakotomamonjy. Non-Negative Tensor Dictionary Learning. European Symposium on Artificial Neural Networks, 2018, Bruges, Belgium. ⟨hal-01721396⟩