DOI: 10.4028/www.scientific.net/amm.513-517.1101
View full text

Abstract: The problem of face classification is essentially a nonlinear subspace classification problem. The features of different face samples lie on different nonlinear subspaces. If the most representative samples of each subspace are selected as the training set, we can enhance the reliability of the classification, as well as reduce the computation. In this paper, a manifold based active learning algorithm, called Laplacian Transductive Optimal Design (LTOD), is presented to select the most representative samples. …

expand abstract