关键词: Fisher线性判别分析, Fisher准则, [L1]范数度量, 鲁棒性, 特征提取

Abstract: Fisher Linear Discriminant Analysis（FLDA） is a classical method of feature extraction with supervised information, which maximizes the Fisher criterion to find the optimal projection matrix. In the criterion of standard FLDA, the involved metric is based on [L2] norm metric, which is usually lack of robustness and sensitive to outliers. In order to improve the robustness, this paper proposes a new model and algorithm for FLDA, which is based on [L1] norm metric. The experimental results show that, FLDA with [L1] norm outperforms that with [L2] norm in classification accuracy and robustness in many cases.

Key words: Fisher linear discriminant analysis, Fisher criterion, [L1] norm metric, robustness, feature extraction