Two-Dimensional Exponential Sparse Discriminant Local Preserving Projections

Abstract

The two-dimensional discriminant locally preserved projections (2DDLPP) algorithm adds a between-class weighted matrix and a within-class weighted matrix into the objective function of the two-dimensional locally preserved projections (2DLPP) algorithm, which overcomes the disadvantage of 2DLPP, i.e., that it cannot use the discrimination information. However, the small sample size (SSS) problem still exists, and 2DDLPP processes the whole original image, which may contain a large amount of redundant information in the retained features. Therefore, we propose a new algorithm, two-dimensional exponential sparse discriminant local preserving projections (2DESDLPP), to address these problems. This integrates 2DDLPP, matrix exponential function and elastic net regression. Firstly, 2DESDLPP introduces the matrix exponential into the objective function of 2DDLPP, making it positive definite. This is an effective method to solve the SSS problem. Moreover, it uses distance diffusion mapping to convert the original image into a new subspace to further expand the margin between labels. Thus more feature information will be retained for classification. In addition, the elastic net regression method is used to find the optimal sparse projection matrix to reduce redundant information. Finally, through high performance experiments with the ORL, Yale and AR databases, it is proven that the 2DESDLPP algorithm is superior to the other seven mainstream feature extraction algorithms. In particular, its accuracy rate is 3.15%, 2.97% and 4.82% higher than that of 2DDLPP in the three databases, respectively.

Keywords

• feature extraction
• SSS problem
• two-dimensional local discriminant preserving projections
• matrix exponential
• elastic net regression