Robust L1-norm two-dimensional linear discriminant analysis. (May 2015)

• Record Type: Journal Article
• Authors: Li, Chun-Na; Shao, Yuan-Hai; Deng, Nai-Yang
• Is Part Of: Neural networks. Volume 65(2015:May)
• Page Start: 92
• Page End: 104
• Publication Date: 2015-05
• Subjects: Linear discriminant analysis -- Two-dimensional linear discriminant analysis -- L1-norm two-dimensional linear discriminant analysis -- Dimensionality reduction -- Iterative technique; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.01.003
• Languages: English
• ISSNs: 0893-6080

Abstract

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.