Reference : Fast Nonlinear Dimensionality Reduction With Topology Preserving Networks
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/11068
Fast Nonlinear Dimensionality Reduction With Topology Preserving Networks
English
Verbeek, J. J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Kröse, B. [> >]
2002
Proceedings of the Tenth European Symposium on Artificial Neural Networks
193-198
Yes
Proceedings of the Tenth European Symposium on Artificial Neural Networks
2002
[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.
http://hdl.handle.net/10993/11068

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