Automatic Relevance Determination in Nonnegative Matrix Factorization

Abstract : Nonnegative matrix factorization (NMF) has become a popular technique for data analysis and dimensionality reduction. However, it is often assumed that the number of latent dimensions (or components) is given. In practice, one must choose a suitable value depending on the data and/or setting. In this paper, we address this important issue by using a Bayesian approach to estimate the latent dimensionality, or equivalently, select the model order. This is achieved via automatic relevance determination (ARD), a technique that has been employed in Bayesian PCA and sparse Bayesian learning. We show via experiments on synthetic data that our technique is able to recover the correct number of components, while it is also able to recover an effective number of components from real datasets such as the MIT CBCL datase
Type de document :
Communication dans un congrès
Rémi Gribonval. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint Malo, United Kingdom. 2009
Liste complète des métadonnées

Littérature citée [9 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00369376
Contributeur : Ist Rennes <>
Soumis le : jeudi 19 mars 2009 - 15:34:46
Dernière modification le : jeudi 11 janvier 2018 - 06:23:38
Document(s) archivé(s) le : vendredi 12 octobre 2012 - 13:50:22

Fichier

17.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00369376, version 1

Citation

Vincent Y. F. Tan, Cédric Févotte. Automatic Relevance Determination in Nonnegative Matrix Factorization. Rémi Gribonval. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint Malo, United Kingdom. 2009. 〈inria-00369376〉

Partager

Métriques

Consultations de la notice

612

Téléchargements de fichiers

1805