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Conference Papers Year : 2013

Estimation de mélange de Gaussiennes sur données compressées

Abstract

Estimating a probability mixture model from a set of vectors typically requires a large amount of memory if the data is voluminous. We propose a framework where the data is jointly compressed to a fixed-size representation called sketch, composed of empirical moments calculated from the data. By analogy with compressive sensing, we derive a parameter estimation algorithm from the sketch. We experimentally show that our algorithm allows precise estimation while consuming less memory than an EM algorithm for voluminous data. The algorithm also provides a privacy-preserving estimation tool since the sketch does not disclose information about individual datum it is based on.
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Dates and versions

hal-00839579 , version 1 (28-06-2013)

Identifiers

  • HAL Id : hal-00839579 , version 1

Cite

Anthony Bourrier, Rémi Gribonval, Patrick Pérez. Estimation de mélange de Gaussiennes sur données compressées. 24ème Colloque Gretsi, Sep 2013, France. pp.191. ⟨hal-00839579⟩
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