Non-linear feature extraction by the coordination of mixture models - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2003

Non-linear feature extraction by the coordination of mixture models

Jakob Verbeek
Nikos Vlassis
  • Fonction : Auteur
  • PersonId : 853678

Résumé

We present a method for non-linear data projection that offers non-linear versions of Principal Component Analysis and Canonical Correlation Analysis. The data is accessed through a probabilistic mixture model only, therefore any mixture model for any type of data can be plugged in. Gaussian mixtures are one example, but mixtures of Bernoulli's to model discrete data might be used as well. The algorithm minimizes an objective function that exhibits one global optimum that can be found by finding the eigenvectors of some matrix. Experimental results on toy data and real data are provided.
Fichier principal
Vignette du fichier
verbeek03asci.pdf (1.3 Mo) Télécharger le fichier
Vignette du fichier
VVK03b.png (79.71 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Figure, Image
Loading...

Dates et versions

inria-00321490 , version 1 (02-02-2011)
inria-00321490 , version 2 (08-03-2011)

Identifiants

  • HAL Id : inria-00321490 , version 2

Citer

Jakob Verbeek, Nikos Vlassis, Ben Krose. Non-linear feature extraction by the coordination of mixture models. 9th Annual Conference of the Advanced School for Computing and Imaging (ASCI '03), Jun 2003, Heijen, Netherlands. pp.287--293. ⟨inria-00321490v2⟩
130 Consultations
332 Téléchargements

Partager

Gmail Facebook X LinkedIn More