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Ye, Qiaolin; Yang, Jian; Liu, Fan; Zhao, Chunxia; Ye, Ning; Yin, Tongming
IEEE transactions on circuits and systems for video technology, 2018-Jan., 2018-1-00, 20180101, Letnik: 28, Številka: 1Journal Article
Recent works have proposed two L1-norm distance measure-based linear discriminant analysis (LDA) methods, L1-LD 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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