Feature Space Distance Metric Learning for Discriminant Graph Embedding

Dimensionality reduction is indispensable for high dimensional data classification. So in this paper, a novel supervised method is developed to reduce dimensions of the original data, which is named feature space distance metric learning (FSDML). Instead of distances between any two points, distances between any two feature spaces are involved in the proposed method. Besides feature space distances(FSD) metric, the inter-class data separablity and the intra-class data locality are all employed for graph embedding, by which a subspace will be explored for data discriminant analysis. The proposed FSDML are evaluated by some state-of-art methods such as linear discriminant analysis (LDA), unsupervised discriminant projection (UDP) and nearest feature space embedding (NFSE). Experiments on some benchmark data sets including AR and ORL face data have shown that the proposed method is effective and efficient.