Non-linear CCA and PCA by Alignment of Local Models

Reference : Non-linear CCA and PCA by Alignment of Local Models


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/11048

Title :	Non-linear CCA and PCA by Alignment of Local Models
Language :	English
Author, co-author :	Verbeek, J. J. [> >]
	Roweis, S. T. [> >]
	Vlassis, Nikos [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Publication date :	2004
Main document title :	Advances in Neural Information Processing Systems 16
Publisher :	Morgan Kaufmann Publishers
Pages :	297-304
Peer reviewed :	Yes
City :	San Mateo
Country :	CA
Event name :	Advances in Neural Information Processing Systems 16.
Event date :	2004
Abstract :	[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.
Permalink :	http://hdl.handle.net/10993/11048
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