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30 March 2000 ANDRomeda: adaptive nonlinear dimensionality reduction
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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.
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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);

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