title: Robust L1-norm two-dimensional linear discriminant analysis.
abstract: In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from the conventional two-dimensional linear discriminant analysis with L2-norm (L2-2DLDA), where the optimization problem is transferred to a generalized eigenvalue problem, the optimization problem in our L1-2DLDA is solved by a simple justifiable iterative technique, and its convergence is guaranteed. Compared with L2-2DLDA, our L1-2DLDA is more robust to outliers and noises since the L1-norm is used. This is supported by our preliminary experiments on toy example and face datasets, which show the improvement of our L1-2DLDA over L2-2DLDA.
authors.affiliation: Zhijiang College, Zhejiang University of Technology, Hangzhou, 310024, PR China.
authors.forename: Chun-Na
authors.initials: CN
authors.lastname: Li
authors.affiliation: Zhijiang College, Zhejiang University of Technology, Hangzhou, 310024, PR China. Electronic address: shaoyuanhai21@163.com.
authors.forename: Yuan-Hai
authors.initials: YH
authors.lastname: Shao
authors.affiliation: College of Science, China Agricultural University, Beijing, 100083, PR China.
authors.forename: Nai-Yang
authors.initials: NY
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016002:Discriminant Analysis; D005145:Face
keywords: Dimensionality reduction; Iterative technique; L1-norm two-dimensional linear discriminant analysis; Linear discriminant analysis; Two-dimensional linear discriminant analysis