Feature extraction of weighted data for implicit variable selection

Abstract

Approaches based on obtaining relevant information from overwhelmingly large sets of measures have been recently adopted as an alternative to specialized features. In this work, we address the problem of finding a relevant subset of features and a suitable rotation (combined feature selection and feature extraction) as a weighted rotation. We focus our attention on two types of rotations: Weighted Principal Component Analysis and Weighted Regularized Discriminant Analysis. The objective function is the maximization of the J4 ratio. Tests were carried out on artificially generated classes, with several non-relevant features. Real data tests were also performed on segmentation of naildfold capillaroscopic images, and NIST-38 database (prototype selection). © Springer-Verlag Berlin Heidelberg 2007.