Bayesian Unsupervised Signal Classification by Dirichlet Process Mixtures of Gaussian Processes
Edmund S. Jackson, Manuel Davy, Arnaud Doucet, William J. Fitzgerald · 2007
This paper presents a Bayesian technique aimed at classifying signals without prior training (clustering). The approach consists of modelling the observed signals, known only through a finite set of samples corrupted by noise, as Gaussian processes. As in many other Bayesian clustering approaches, the clusters are defined thanks to a mixture model. In order to estimate the number of clusters, we assume a priori a countably infinite number of clusters, thanks to a Dirichlet process model over the Gaussian processes parameters. Computations are performed thanks to a dedicated Monte Carlo Markov Chain algorithm, and results involving real signals (mRNA expression profiles) are presented.