[en] 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.
Disciplines :
Sciences informatiques
Identifiants :
UNILU:UL-ARTICLE-2011-733
Auteur, co-auteur :
Verbeek, J. J.
Roweis, S. T.
VLASSIS, Nikos ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Langue du document :
Anglais
Titre :
Non-linear CCA and PCA by Alignment of Local Models
Date de publication/diffusion :
2004
Nom de la manifestation :
Advances in Neural Information Processing Systems 16.
Date de la manifestation :
2004
Titre de l'ouvrage principal :
Advances in Neural Information Processing Systems 16
Maison d'édition :
Morgan Kaufmann Publishers, San Mateo, Etats-Unis - Californie
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