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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Applications of Online Nonnegative Matrix Factorization to Image and Time-Series Data
Online nonnegative matrix factorization (ONMF) is a matrix factorization technique in the online setting where data are acquired in a streaming fashion and the matrix factors are updated each time. This enables factor an…
Dictionary LearningTime SeriesTime Series AnalysisProbabilistic semi-nonnegative matrix factorization: a Skellam-based framework
We present a new probabilistic model to address semi-nonnegative matrix factorization (SNMF), called Skellam-SNMF. It is a hierarchical generative model consisting of prior components, Skellam-distributed hidden variable…
Bayesian InferenceA Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes
Parametric policy search algorithms are one of the methods of choice for the optimisation of Markov Decision Processes, with Expectation Maximisation and natural gradient ascent being considered the current state of the …
Nonnegative HMM for Babble Noise Derived from Speech HMM: Application to Speech Enhancement
Deriving a good model for multitalker babble noise can facilitate different speech processing algorithms, e.g. noise reduction, to reduce the so-called cocktail party difficulty. In the available systems, the fact that t…
Speech EnhancementAlgorithms for audio inpainting based on probabilistic nonnegative matrix factorization
Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propose to structure the spectrogram with non…
Audio inpainting