Context-GMM: Incremental learning of sparse priors for Gaussian mixture regression

Gaussian Mixture Models have been widely used in robotic control and in sensory anticipation applications. A mixture model is learnt from demonstrations and later used to infer the most likely control signals, or is also used as a forward model to predict the change in sensory signals over time. However, such models often are too big to be tractable in real-time applications. In this paper we introduce the Context-GMM, a method to learn sparse priors over the mixture components. Such priors are stable over large amounts of time and provide a way of selecting very small subsets of mixture components without significant loss in accuracy and with huge computational savings.