Fast nonlinear dimensionality reduction with topology preserving networks

Abstract : 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 quan- tizers and the number of data points. The quantizers and neighborhood structure are use to map the data to the low dimensional space.
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Communication dans un congrès
Michel Verleysen. 10th Eurorean Symposium on Artificial Neural Networks (ESANN '02), Apr 2002, Bruges, Belgium. 2002
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  • HAL Id : inria-00321500, version 1

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Jakob Verbeek, Nikos Vlassis, Ben Krose. Fast nonlinear dimensionality reduction with topology preserving networks. Michel Verleysen. 10th Eurorean Symposium on Artificial Neural Networks (ESANN '02), Apr 2002, Bruges, Belgium. 2002. 〈inria-00321500〉

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