文献类型：期刊文献

英文题名：Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis

作者：Ye, Qiaolin[1,2] Fu, Liyong[1] Zhang, Zhao[3] Zhao, Henghao[2] Naiem, Meem[2]

第一作者：Ye, Qiaolin

通信作者：Fu, LY[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Jiangsu, Peoples R China;[3]Soochow Univ, Coll Comp Sci & Technol, Nanjing 215006, Jiangsu, Peoples R China

年份：2018

卷号：105

起止页码：393-404

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20182605360374);Scopus(收录号：2-s2.0-85048839092);WOS:【SCI-EXPANDED(收录号:WOS:000441874700032)】；

基金：The work was supported in part by the Central Public-interest Scientific Institution Basal Research Fund under Grant CAFYBB2016SZ003, the National Science Foundation of China under Grants 61401214 and 61773210, and the Natural Science Foundation of Jiangsu Province under Grant BK20171453.

语种：英文

外文关键词：linear Discriminant Analysis; Lp-norm; Ls-norm; Robustness

摘要：Recently, L1-norm distance measure based Linear Discriminant Analysis (LDA) techniques have been shown to be robust against outliers. However, these methods have no guarantee of obtaining a satisfactory-enough performance due to the insufficient robustness of L1-norm measure. To mitigate this problem, inspired by recent works on Lp-norm based learning, this paper proposes a new discriminant method, called Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis (FLDA-Lsp). The proposed method achieves robustness by replacing the L2-norm within-and between-class distances in conventional LDA with Lp-and Ls-norm ones. By specifying the values of p and s, many of previous efforts can be naturally expressed by our objective. The requirement of simultaneously maximizing and minimizing a number of Lp-and Ls-norm terms results in a difficulty to the optimization of the formulated objective. As one of the important contributions of this paper, we design an efficient iterative algorithm to address this problem, and also conduct some insightful analysis on the existence of local minimum and the convergence of the proposed algorithm. Theoretical insights of our method are further supported by promising experimental results on several images databases. (c) 2018 Elsevier Ltd. All rights reserved.