Dimensionality Reduction, Classification and Reconstruction Problems in Statistical Learning Approaches

Gilson A. Giraldi, Paulo S. Rodrigues, Edson C. Kitani, Carlos E. Thomaz

Abstract


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.



DOI: https://doi.org/10.22456/2175-2745.6016

Copyright (c) 2018 Gilson A. Giraldi, Paulo S. Rodrigues, Edson C. Kitani, Carlos E. Thomaz

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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