An Online Expectation-Maximisation Algorithm for Nonnegative Matrix Factorisation Models

In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters. We also propose a sequential Monte Carlo approximation of our online EM algorithm. We show the performance of the proposed method with two numerical examples.

[1]  Sumeetpal S. Singh,et al.  Forward Smoothing using Sequential Monte Carlo , 2010, 1012.5390.

[2]  Ali Taylan Cemgil,et al.  Bayesian Inference for Nonnegative Matrix Factorisation Models , 2009, Comput. Intell. Neurosci..

[3]  D. Pierre Forward Smoothing Using Sequential Monte Carlo , 2009 .

[4]  Ali Taylan Cemgil,et al.  Nonnegative matrix factorizations as probabilistic inference in composite models , 2009, 2009 17th European Signal Processing Conference.

[5]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[6]  Gianluigi Mongillo,et al.  Online Learning with Hidden Markov Models , 2008, Neural Computation.

[7]  Francis R. Bach,et al.  Online Learning for Latent Dirichlet Allocation , 2010, NIPS.

[8]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[9]  Jason J. Ford,et al.  On‐line almost‐sure parameter estimation for partially observed discrete‐time linear systems with known noise characteristics , 2002 .

[10]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[11]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[12]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[13]  Geoffrey J. Gordon,et al.  A Unified View of Matrix Factorization Models , 2008, ECML/PKDD.

[14]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[15]  Télécom ParisTech ONLINE SEQUENTIAL MONTE CARLO EM ALGORITHM , 2009 .

[16]  Olivier Capp'e Online EM Algorithm for Hidden Markov Models , 2009, 0908.2359.

[17]  Arnaud Doucet,et al.  An overview of sequential Monte Carlo methods for parameter estimation in general state-space models , 2009 .

[18]  Serhat Selcuk Bucak,et al.  Incremental subspace learning via non-negative matrix factorization , 2009, Pattern Recognit..

[19]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.