An Online Expectation-Maximisation Algorithm for Nonnegative Matrix Factorisation Models
This work addresses the need for efficient online algorithms in NMF for applications like signal processing or data analysis, but it appears incremental as it builds on existing EM and NMF methods.
The paper tackled the problem of nonnegative matrix factorization (NMF) by formulating it as a maximum likelihood estimation for hidden Markov models and proposed online expectation-maximisation (EM) algorithms, including a sequential Monte Carlo approximation, with performance demonstrated in two numerical examples.
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.