J
Jean-François Paiement
Researcher at Université de Montréal
Publications - 4
Citations - 461
Jean-François Paiement is an academic researcher from Université de Montréal. The author has contributed to research in topics: Spectral clustering & Kernel principal component analysis. The author has an hindex of 4, co-authored 4 publications receiving 431 citations. Previous affiliations of Jean-François Paiement include Centre de Recherches Mathématiques.
Papers
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Journal ArticleDOI
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Yoshua Bengio,Olivier Delalleau,Nicolas Le Roux,Jean-François Paiement,Pascal Vincent,Marie Ouimet +5 more
TL;DR: A direct relation is shown between spectral embedding methods and kernel principal components analysis and how both are special cases of a more general learning problem: learning the principal eigenfunctions of an operator defined from a kernel and the unknown data-generating density.
Posted Content
Spectral Clustering and Kernel PCA are Learning Eigenfunctions
Yoshua Bengio,Pascal Vincent,Jean-François Paiement,Olivier Delalleau,Marie Claude Ouimet,Nicolas Le Roux +5 more
TL;DR: In this article, the authors show a direct equivalence between spectral clustering and kernel PCA, and how both are special cases of a more general learning problem, that of learning the principal eigenfunctions of a kernel, when the functions are from a function space whose scalar product is defined with respect to a density model.
Book ChapterDOI
Spectral Dimensionality Reduction
Yoshua Bengio,Olivier Delalleau,Nicolas Le Roux,Jean-François Paiement,Pascal Vincent,Marie Claude Ouimet +5 more
TL;DR: A number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian Eigenmaps and kernel PCA, which are based on performing an eigen-decomposition are put under a common framework.
Posted Content
Spectral Dimensionality Reduction
Yoshua Bengio,Olivier Delalleau,Nicolas Le Roux,Jean-François Paiement,Pascal Vincent,Marie Claude Ouimet +5 more
TL;DR: In this article, the authors put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian Eigenmaps and kernel PCA, which are based on performing an eigendecomposition.