Compressive Gaussian Mixture Estimation by Orthogonal Matching Pursuit with Replacement - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Documents Associated With Scientific Events Year :

Compressive Gaussian Mixture Estimation by Orthogonal Matching Pursuit with Replacement

Abstract

This work deals with the problem of fitting a Gaussian Mixture Model (GMM) to a large collection of data. Usual approaches such as the classical Expectation Maximization (EM) algorithm are known to perform well but require extensive access to the data. The proposed method compresses the entire database into a single low-dimensional sketch that can be computed in one pass then directly used for GMM estimation. This sketch can be seen as resulting from the application of a linear operator to the underlying probability distribution, thus establishing a connection between our method and generalized compressive sensing. In particular, the new algorithms introduced to estimate GMMs are similar to usual greedy algorithms in compressive sensing.
Fichier principal
Vignette du fichier
spars_abstract.pdf (829.25 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01165984 , version 1 (21-06-2015)

Identifiers

  • HAL Id : hal-01165984 , version 1

Cite

Nicolas Keriven, Rémi Gribonval. Compressive Gaussian Mixture Estimation by Orthogonal Matching Pursuit with Replacement. SPARS 2015, Jul 2015, Cambridge, United Kingdom. ⟨hal-01165984⟩
271 View
263 Download

Share

Gmail Facebook Twitter LinkedIn More