Dimensionality Reduction Based on Neighborhood Preserving and Marginal Discriminant Embedding

Abstract

Data with high dimensionality often occurs, which will produce large time and energy overheads when directly used in classification tasks. So, as one of the most important fields in machine learning, dimensionality reduction has been paid more and more attention and has achieved a prodigious progress in the theory and algorithm research. Linear Graph embedding (LGE) model is an efficient tool for dimensionality reduction. According to the problems of supervised dimensionality reduction with Non-Gaussian data distributions and at the same time consider neighborhood preserving relations among samples, a novel subspace learning method, neighborhood preserving and marginal discriminant embedding (NP-MDE), is proposed based on LGE and marginal Fisher analysis in this paper. NP-MDE could minimize the within-class scatter and meanwhile maximize the margin among different classes. Moreover, the neighborhood structure with each class is preserved. Experiments on Yale face image data sets show that after dimensionality reduction using NP-MDE, the average classification accuracy is very good.