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Proceedings Paper

ANDRomeda: adaptive nonlinear dimensionality reduction
Author(s): David J. Marchette; Carey E. Priebe
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Paper 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.

Paper Details

Date Published: 30 March 2000
PDF: 7 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380564
Show Author Affiliations
David J. Marchette, Naval Surface Warfare Ctr. (United States)
Carey E. Priebe, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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