Supervised classification for functional data: A weighted distance approach
AbstractA natural methodology for discriminating functional data is based on the distances from the observation or its derivatives to group representative functions (usually the mean) or their derivatives. It is proposed to use a combination of these distances for supervised classification. Simulation studies show that this procedure performs very well, resulting in smaller testing classification errors. Applications to real data show that this technique behaves as well as–and in some cases better than–existing supervised classification methods for functions.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 7 ()
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Web page: http://www.elsevier.com/locate/csda
Supervised classification; Discriminant analysis; Functional data; Weighted distances;
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