Random Moments for Sketched Mixture Learning
Abstract
We present a method to solve large-scale mixture learning tasks from a sketch of the data, formed by random generalized empirical moments. We give empirical and theoretical results on k-means and Gaussian Mixture Model estimation problems.
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