Non-linear CCA and PCA by alignment of local models - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2004

Non-linear CCA and PCA by alignment of local models

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

Résumé

We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of PCA, our work extends recent methods for non-linear dimensionality reduction to the case where multiple embeddings of the same underlying low dimensional coordinates are observed, each lying on a different high dimensional manifold. We also show that a special case of our method, when applied to only a single manifold, reduces to the Laplacian Eigenmaps algorithm. As with previous alignment schemes, once the mixture models have been estimated, all of the parameters of our model can be estimated in closed form without local optima in the learning. Experimental results illustrate the viability of the approach as a non-linear extension of CCA.
Fichier principal
Vignette du fichier
verbeek04nips.pdf (480.11 Ko) Télécharger le fichier
Vignette du fichier
VRV04.png (142.31 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Figure, Image
Loading...

Dates et versions

inria-00321485 , version 1 (02-02-2011)
inria-00321485 , version 2 (05-04-2011)

Identifiants

  • HAL Id : inria-00321485 , version 2

Citer

Jakob Verbeek, Sam Roweis, Nikos Vlassis. Non-linear CCA and PCA by alignment of local models. Seventeenth Annual Conference on Neural Information Processing Systems (NIPS '03), Dec 2003, Vancouver, Canada. pp.297--304. ⟨inria-00321485v2⟩
182 Consultations
141 Téléchargements

Partager

Gmail Facebook X LinkedIn More