On simultaneous selection of prototypes and features in large data

Abstract

In dealing with high-dimensional, large data, for the sake of abstract generation one resorts to either dimensionality reduction or cluster the patterns and deal with cluster representatives or both. The current paper examines whether there exists an equivalence in terms of generalization error. Four different approaches are followed and results of exercises are provided in driving home the issues involved. © Springer-Verlag Berlin Heidelberg 2005.