[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.
Disciplines :
Computer science
Identifiers :
UNILU:UL-ARTICLE-2011-740
Author, co-author :
Verbeek, J. J.
VLASSIS, Nikos ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Kröse, B.
Language :
English
Title :
Fast Nonlinear Dimensionality Reduction With Topology Preserving Networks
Publication date :
2002
Event name :
Proceedings of the Tenth European Symposium on Artificial Neural Networks
Event date :
2002
Main work title :
Proceedings of the Tenth European Symposium on Artificial Neural Networks
