Fast Nonlinear Dimensionality Reduction With Topology Preserving Networks

Reference : Fast Nonlinear Dimensionality Reduction With Topology Preserving Networks


	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/10993/11068

Title :	Fast Nonlinear Dimensionality Reduction With Topology Preserving Networks
Language :	English
Author, co-author :	Verbeek, J. J. [> >]
	Vlassis, Nikos [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Kröse, B. [> >]
Publication date :	2002
Main document title :	Proceedings of the Tenth European Symposium on Artificial Neural Networks
Pages :	193-198
Peer reviewed :	Yes
Event name :	Proceedings of the Tenth European Symposium on Artificial Neural Networks
Event date :	2002
Abstract :	[en] We present a fast alternative for the Isomap algorithm. A set of quantizers is fit to the data and a neighborhood structure based on the competitive Hebbian rule is imposed on it. This structure is used to obtain low-dimensional description of the data by means of computing geodesic distances and multi dimensional scaling. The quantization allows for faster processing of the data. The speed-up as compared to Isomap is roughly quadratic in the ratio between the number of quantizers and the number of data points.
Permalink :	http://hdl.handle.net/10993/11068

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