Geodesic Gabriel Graph Based Supervised Nonlinear Manifold Learning

Abstract

As for the discriminant analysis on nonlinear manifold, a geodesic Gabriel graph based supervised manifold learning algorithm was proposed. Using geodesic distance to discover the intrinsic geometry of the manifold, the geodesic Gabriel graph was constructed to locate the key local regions where the local linear classifiers would be learned. The global nonlinear classifier was achieved by merging the multiple local classifiers applying the soft margin criterion, which assigned the optimal weight to each local classifier in an iterative way without any assumption of the distribution of the example data. The superiority of this algorithm is confirmed by experiments on synthesized data and face image databases.