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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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https://hal.inria.fr/hal-01494045
Contributor : Nicolas Keriven <>
Submitted on : Wednesday, March 22, 2017 - 3:41:14 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:43 PM
Document(s) archivé(s) le : Friday, June 23, 2017 - 1:46:10 PM

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  • HAL Id : hal-01494045, version 1

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Nicolas Keriven, Rémi Gribonval, Gilles Blanchard, Yann Traonmilin. Random Moments for Sketched Mixture Learning. SPARS2017 - Signal Processing with Adaptive Sparse Structured Representations workshop, Jun 2017, Lisbon, Portugal. ⟨hal-01494045⟩

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