Gradient-based training of Gaussian Mixture Models in High-Dimensional Spaces

18 Dec 2019Alexander GepperthBenedikt Pfülb

We present an approach for efficiently training GMMs solely with Stochastic Gradient Descent (SGD) on huge amounts of non-stationary, high-dimensional data. In such scenarios, SGD is superior to the traditionally Expectation-Maximization (EM) algorithm w.r.t... (read more)

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