ANDRomeda: adaptive nonlinear dimensionality reduction

David J. Marchette; Carey E. Priebe

doi:10.1117/12.380564

30 March 2000 ANDRomeda: adaptive nonlinear dimensionality reduction

David J. Marchette, Carey E. Priebe

Proceedings Volume 4055, Applications and Science of Computational Intelligence III; (2000) https://doi.org/10.1117/12.380564
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

Standard approaches for the classification of high dimensional data require the selection of features, the projection of the features to a lower dimensional space, and the construction of the classifier in the lower dimensional space. Two fundamental issues arise in determining an appropriate projection to a lower dimensional space: the target dimensionality for the projection must be determined, and a particular projection must be selected from a specified family. We present an algorithm which is designed specifically for classification task and addresses both these issues. The family of nonlinear projections considered is based on interpoint distances - in particular, we consider point-to-subset distances. Our algorithm selects both the number of subsets to use and the subsets themselves. The methodology is applied to an artificial nose odorant classification task.

Citation Download Citation

David J. Marchette and Carey E. Priebe "ANDRomeda: adaptive nonlinear dimensionality reduction", Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); https://doi.org/10.1117/12.380564

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