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 ![]() | |
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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