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stat.ML2025★ 1 cited
Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters
Sebastian Salwig, Till Kahlke, Florian Hirschberger +2
Gaussian Mixture Models (GMMs) range among the most frequently used models in machine learning. However, training large, general GMMs becomes computationally prohibitive for data s…
stat.ML2018★ 8 cited
Large Scale Clustering with Variational EM for Gaussian Mixture Models
Florian Hirschberger, Dennis Forster, Jörg Lücke
This paper represents a preliminary (pre-reviewing) version of a sublinear variational algorithm for isotropic Gaussian mixture models (GMMs). Further developments of the algorithm…