Estimation de mélange de Gaussiennes sur données compressées - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2013

Estimation de mélange de Gaussiennes sur données compressées


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.
Fichier principal
Vignette du fichier
bourrier191.pdf (348.13 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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


  • HAL Id : hal-00839579 , version 1


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


Gmail Facebook X LinkedIn More