Reference : Non-linear CCA and PCA by Alignment of Local Models
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/11048
Non-linear CCA and PCA by Alignment of Local Models
English
Verbeek, J. J. [> >]
Roweis, S. T. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2004
Advances in Neural Information Processing Systems 16
Morgan Kaufmann Publishers
297-304
Yes
San Mateo
CA
Advances in Neural Information Processing Systems 16.
2004
[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.
http://hdl.handle.net/10993/11048
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