Supervised classification for functional data: A weighted distance approach
A 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 asand in some cases better thanexisting supervised classification methods for functions.
supervised classification functional data weighted distances discriminant analysis