期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 28, 期 1, 页码 114-129出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2016.2596158
关键词
Iterative framework; L1-ELDA; L1-norm distance measure-based linear discriminant analysis (LDA); robustness
资金
- National Foundation for Distinguished Young Scientists, China [31125008]
- Natural Science Foundation of the Jiangsu Higher Education Institutions, China [14KJB520018]
- National Science Foundation of China [61101197, 61272220, 61401214]
Recent works have proposed two L1-norm distance measure-based linear discriminant analysis (LDA) methods, L1-LDA and LDA-L1, which aim to promote the robustness of the conventional LDA against outliers. In LDA-L1, a gradient ascending iterative algorithm is applied, which, however, suffers from the choice of stepwise. In L1-LDA, an alternating optimization strategy is proposed to overcome this problem. In this paper, however, we show that due to the use of this strategy, L1-LDA is accompanied with some serious problems that hinder the derivation of the optimal discrimination for data. Then, we propose an effective iterative framework to solve a general L1-norm minimization-maximization (minmax) problem. Based on the framework, we further develop a effective L1-norm distance-based LDA (called L1-ELDA) method. Theoretical insights into the convergence and effectiveness of our algorithm are provided and further verified by extensive experimental results on image databases.
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