Computational Intelligence and Neuroscience
Volume 2008 (2008), Article ID 947438, 8 pages
doi:10.1155/2008/947438
Research Article

Probabilistic Latent Variable Models as Nonnegative Factorizations

Madhusudana Shashanka,1 Bhiksha Raj,2 and Paris Smaragdis3

1Mars Incorporated, 800 High Street, Hackettstown, New Jersy 07840, USA
2Mitsubishi Electric Research Laboratories, 201 Broadway, Cambridge MA 02139, USA
3Adobe Systems Incorporated, 275 Grove Street, Newton MA 02466, USA

Received 21 December 2007; Accepted 13 February 2008

Academic Editor: Rafal Zdunek

Copyright © 2008 Madhusudana Shashanka et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity constraints. We argue through these extensions that the use of this approach allows for rapid development of complex statistical models for analyzing nonnegative data.