Discriminant adaptive edge weights for graph embedding

Abstract

Many linear dimensionality reduction (LDR) methods, such as PCA and LDA, can be reformulated in the framework of graph embedding (GE). In this framework, those LDR methods are differentiated by values of edge weights of a graph. This paper first proposes a linear dimensionality reduction method, which assigns edges with discriminant adaptive weights. Specifically, we compute a local decision hyper-plane by using support vector machine (SVM). Then edge weighs corresponding to the local region are expressed as a function of the angle between the direction of the edges and the normal vector of the hyper-plane. Experimental results demonstrate the advantages of this proposed method.