Gaussian mixture learning via robust competitive agglomeration

When learning Gaussian mixtures from multivariate data, it is crucial to select the appropriate number of components and simultaneously avoid local optima. To resolve these problems, we follow the idea of competitive agglomeration which is originally used for fuzzy clustering and propose two robust algorithms for Gaussian mixture learning. Through some asymptotic analysis, we find that such robust competitive agglomeration can lead to automatic model selection on Gaussian mixtures and also make our algorithms less sensitive to initialization than the EM algorithm. Experiments demonstrate that our algorithms can achieve promising results just as our theoretic analysis.