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From Adaptive Kernel Density Estimation to Sparse Mixture Models
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 . This semi-parametric method bridges from
non-parametric adaptive kernel density estimation (KDE) to parametric ordinary
least-squares when . Experiments show that simpler sparse mixture
models retain the level of details present in the adaptive KDE solution.Comment: in Proceedings of iTWIST'18, Paper-ID: 20, Marseille, France,
November, 21-23, 201