From Adaptive Kernel Density Estimation to Sparse Mixture Models
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We introduce a balloon estimator in a generalized expectation-maximization method for estimating all parameters of a Gaussian mixture model given one data sample per mixture component. Instead of limiting explicitly the model size, this regularization strategy yields low-complexity sparse models where the number of effective mixture components reduces with an increase of a smoothing probability parameter P>0. This semi-parametric method bridges from non-parametric adaptive kernel density estimation (KDE) to parametric ordinary least-squares when P=1. Experiments show that simpler sparse mixture models retain the level of details present in the adaptive KDE solution.
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