On learning statistical mixtures maximizing the complete likelihood

Statistical mixtures are semi-parametric models ubiquitously met in data science since they can universally model smooth densities arbitrarily closely. Finite mixtures are usually inferred from data using the celebrated Expectation-Maximization framework that locally iteratively maximizes the incomplete likelihood by assigning softly data to mixture components. In this paper, we present a novel methodology to infer mixtures by transforming the learning problem into a sequence of geometric center-based hard clustering problems that provably maximizes monotonically the complete likelihood. Our versatile method is fast and uses low memory footprint: The core inner steps can be implemented using various generalized k-means type heuristics. Thus we can leverage recent results on clustering to mixture learning. In particular, for mixtures of singly-parametric distributions including for example the Rayleigh, Weibull, or Poisson distributions, we show how to use dynamic programming to solve exactly the inner geometric clustering problems. We discuss on several extensions of the methodology.