Double adjacency graphs-based discriminant neighborhood embedding

Abstract

Discriminant neighborhood embedding (DNE) is a typical graph-based dimensionality reduction method, and has been successfully applied to face recognition. By constructing an adjacency graph, aiming to keep the local structure for original data in the subspace, it is able to find the optimal discriminant direction effectively. Not for every sample does DNE set up a link between it and its heterogeneous samples when constructing the adjacency graph, which would result in a small between-class scatter. Motivated by this fact, we develop an extension of DNE, called double adjacency graphs-based discriminant neighborhood embedding (DAG-DNE) by introducing two adjacency graphs, or homogeneous and heterogeneous neighbor adjacency graphs. In DAG-DNE, neighbors belonging to the same class are compact while neighbors belonging to different classes become separable in the subspace. Thus, DAG-DNE could keep the local structure of a given data and find a good projection matrix for them. To investigate the performance of DAG-DNE, we compare it with the state-of-the-art dimensionality reduction techniques such as DNE and MFA on several publicly available datasets. Experimental results show the feasibility and effectiveness of the proposed method.