Nuclear-norm based 2DLDA with application to face recognition

Two-dimensional Linear Discriminant analysis (2DLDA) has been widely used for dimensionality reduction and feature extraction of data. However, most existing 2DLDA based methods cannot well characterize the spatial geometric structure of image. To solve this problem, inspired by the fact that nuclear-norm well reveals spatial relationship among pixels in each image and is robust to illumination and noise, we propose a nuclear-norm based two-dimensional Linear Discriminant analysis (2DLDA-nuclear), which employs nuclear-norm to characterize within-class and between-class scatters. Compared with most existing robust 2DLDA methods, our method not only helps suppress noise and illumination but also preserves spatial geometric structure of image. We also provide an effective iterative algorithm to solve 2DLDA-nuclear. Experiments on five public image databases show the effectiveness of our model.