Unsupervised learning of regression mixture models with unknown number of components

Unsupervised learning of regression mixture models with unknown number of components

Regression mixture models are widely studied in statistics, machine learning and data analysis. Fitting regression mixtures is challenging and is usually performed by maximum likelihood by using the expectation-maximization (EM) algorithm. However, it is well-known that the initialization is crucial for EM. If the initialization is inappropriately performed, the EM algorithm may lead to unsatis...

Overfitting Bayesian Mixture Models with an Unknown Number of Components

This paper proposes solutions to three issues pertaining to the estimation of finite mixture models with an unknown number of components: the non-identifiability induced by overfitting the number of components, the mixing limitations of standard Markov Chain Monte Carlo (MCMC) sampling techniques, and the related label switching problem. An overfitting approach is used to estimate the number of...

Physical Mixture Modeling with Unknown Number of Components

Measured physical spectra often comprise an unknown number of components of known parametric family. A reversible jump Markov chain Monte Carlo (RJMCMC) technique is applied to the problem of estimating the number of components evident in the data jointly with the parameters of the components. The physical model consists of a mixture of components, an additive background, and a convolution with...

Multivariate Gaussian mixture modeling with unknown number of components

Particle filters for mixture models with an unknown number of components

We consider the analysis of data under mixture models where the number of components in the mixture is unknown. We concentrate on mixture Dirichlet process models, and in particular we consider such models under conjugate priors. This conjugacy enables us to integrate out many of the parameters in the model, and to discretize the posterior distribution. Particle filters are particularly well su...