Reference : Coordinating Principal Component Analyzers
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/11070
Coordinating Principal Component Analyzers
English
Verbeek, Jakob J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Kröse, Ben J. A. [> >]
2002
Proc. Int. Conf. on Artificial Neural Networks, Madrid, Spain,
Springer
914-919
Yes
Berlin
Germany
Proc. Int. Conf. on Artificial Neural Networks, Madrid, Spain,
2002
[en] Mixtures of Principal Component Analyzers can be used to model high dimensional data that lie on or near a low dimensional manifold. By linearly mapping the PCA subspaces to one global low dimensional space, we obtain a `global' low dimensional coordinate system for the data. As shown by Roweis et al., ensuring consistent global low-dimensional coordinates for the data can be expressed as a penalized likelihood optimization problem. We show that a restricted form of the Mixtures of Probabilistic PCA model allows for a more efficient algorithm. Experimental results are provided to illustrate the viability method.
http://hdl.handle.net/10993/11070

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
download.pdfAuthor postprint251.77 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.