2DUDP: Novel method of feature extraction based on image matrix
Li Yongzhi, Guangming He, Yang Jingyu · 2008
This paper presents a new method of dimensionality reduction of high dimensional data. The new discriminant criterion function be characterized by between the nonlocal scatter and the local scatter, and to directly construct between local scatter matrix and nonlocal scatter matrix by sample image matrixes. The criterion main purpose is to find a group of projection axis that simultaneously maximizes the nonlocal scatter and minimizes the local scatter of sample feature. The experimental results on YALE face database and AR face database show that the proposed method consistently outperforms LPP and UDP based on image vector, and even outperforms LDA.