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Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling

Abstract : We provide statistical learning guarantees for two unsupervised learning tasks in the context of compressive statistical learning, a general framework for resource-efficient large-scale learning that we introduced in a companion paper. The principle of compressive statistical learning is to compress a training collection, in one pass, into a low-dimensional sketch (a vector of random empirical generalized moments) that captures the information relevant to the considered learning task. We explicit random feature functions which empirical averages preserve the needed information for compressive clustering and compressive Gaussian mixture modeling with fixed known variance, and establish sufficient sketch sizes given the problem dimensions.
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https://hal.inria.fr/hal-02536818
Contributor : Rémi Gribonval <>
Submitted on : Thursday, April 16, 2020 - 7:10:23 PM
Last modification on : Saturday, July 11, 2020 - 3:14:13 AM

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  • HAL Id : hal-02536818, version 1
  • ARXIV : 2004.08085

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Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin. Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling. 2020. ⟨hal-02536818⟩

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