Bound constrained weighted NMF for industrial source apportionment

Abdelhakim Limem (1) , Matthieu Puigt (1) , Gilles Delmaire (1) , Gilles Roussel (1) , Dominique Courcot (2)

1 LISIC - Laboratoire d'Informatique Signal et Image de la Côte d'Opale
2 UCEIV - Unité de Chimie Environnementale et Interactions sur le Vivant

In our recent work, we introduced a constrained weighted Non-negative Matrix Factorization (NMF) method using a β-divergence cost function. We assumed that some components of the factorization were known and were used to inform our NMF algorithm. In this paper, we are provided some intervals of possible values for some factorization components. We thus introduce an extended version of our previous work combining an improved divergence expression and some matrix normalizationswhile using the known / bounded information. Some experiments on simulated mixtures of particulate matter sources show the relevance of these approaches.

Mots clés: Beta divergence; Non-negative matrix factorization; Blind source separation; Normalization

Domaines: Traitement du signal et de l'image [eess.SP]

Abdelhakim Limem, Matthieu Puigt, Gilles Delmaire, Gilles Roussel, Dominique Courcot. Bound constrained weighted NMF for industrial source apportionment. 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2014), Sep 2014, Reims, France. ⟨10.1109/MLSP.2014.6958851⟩. ⟨hal-01367328⟩