title: Fitting large mixture models using stochastic component selection
abstract: Traditional methods for unsupervised learning of finite mixture models require evaluating the likelihood of all components of the mixture. This becomes computationally prohibitive when the number of components is large, as it is in the sum-product (transform) networks. Therefore, we propose an approach combining the expectation-maximization and the Metropolis-Hastings algorithm to evaluate only a lower number of, stochastically sampled, components, thus substantially reducing the computational cost. We put emphasis on the generality of our method, equipping it with the ability to train both shallow and deep mixture models which involve complex, and possibly nonlinear, transformations. The performance of our method is illustrated in a variety of synthetic and real-data contexts, considering deep models, such as mixtures of normalizing flows and sum-product (transform) networks.
authors: Milan Papez Tomáš Pevný Vaclav Smidl
hIndex: Tomáš Pevný 30