Dimensionality Reduction, Classification and Reconstruction Problems in Statistical Learning Approaches
Gilson Antonio Giraldi, Paulo Sergio Rodrigues, Edson Caoru Kitani, Carlos Eduardo Thomaz · Revista de Informática Teórica e Aplicada · 2008
Statistical learning theory explores ways of estimating functional dependency from a given collection of data. The specific sub-area of supervised statistical learning covers important models like Perceptron, Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). In this paper we review the theory of such models and compare their separating hypersurfaces for extracting group-differences between samples. Classification and reconstruction are the main goals of this comparison. We show recent advances in this topic of research illustrating their application on face and medical image databases.