Estudio Comparativo de Métodos de Selección de Características de Inferencia Supervisada y No Supervisada

Abstract

In this work, a comparative study of feature selection methods for supervised and unsupervised inference obtained from classical PCA is presented. We deduce an expression for the cost function of PCA based on the mean square error of data and its orthonormal projection, and then this concept is extended to obtain an expression for general WPCA. Additionally, we study the supervised and unsupervised Q – α algorithm and its relation with PCA. At the end, we present results employing two data sets: A low-dimensional data set to analyze the effects of orthonormal rotation, and a highdimensional data set to assess the classification performance. The feature selection methods were assessed taking into account the number of relevant features, computational cost and classification performance. The classification was carried out using a partitional clustering algorithm.