Geometric Data Analysis Based on Manifold Learning with Applications for Image Understanding Gastao˜ Florencioˆ Miranda Junior Carlos Eduardo Thomaz Gilson Antonio Giraldi Department of Mathematics Department of Electrical Engineering Depart. of Math. and Comput. Methods Federal University of Sergipe FEI National Laboratory for Aracaju, Brazil Sao˜ Bernardo do Campo, Brazil Scientific Computing (LNCC) Email:
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[email protected] Abstract—Nowadays, pattern recognition, computer vision, the classical principal component analysis (PCA), factor signal processing and medical image analysis, require the man- analysis (FA) [3], multidimensional scaling (MDS) [4], [5] aging of large amount of multidimensional image databases, and projection pursuit (PP) [3], [6]. Linear techniques seek possibly sampled from nonlinear manifolds. The complex tasks involved in the analysis of such massive data lead to a strong for new variables that obey some optimization criterium and demand for nonlinear methods for dimensionality reduction to can be expressed as linear combination of the original ones. achieve efficient representation for information extraction. In That’s why they fail if the input data has curved or nonlinear this avenue, manifold learning has been applied to embed non- structures. linear image data in lower dimensional spaces for subsequent Nonlinear dimensionality reduction methods include ker- analysis. The result allows a geometric interpretation of image spaces with relevant consequences for data topology, compu- nel approaches, like kernel PCA (KPCA), kernel LDA tation of image similarity, discriminant analysis/classification (KLDA) and kernel Fisher discriminant analysis (KFD). tasks and, more recently, for deep learning issues. In this These techniques map the original input data into a feature paper, we firstly review Riemannian manifolds that compose space by a (global) nonlinear mapping, where inner products the mathematical background in this field.