A new linear discriminant analysis algorithm based on L1-norm maximization and locality preserving projection

作者：Zhang, Di^*; Li, Xueqiang; He, Jiazhong; Du, Minghui

来源：Pattern Analysis and Applications, 2018, 21(3): 685-701.

DOI：10.1007/s10044-017-0594-y

• Feature extraction
• Linear discriminant analysis
• Local information
• L1-norm
• L2-norm
• Pattern classification

Generic L2-norm-based linear discriminant analysis (LDA) is sensitive to outliers and only captures global structure information of sample points. In this paper, a new LDA-based feature extraction algorithm is proposed to integrate both global and local structure information via a unified L1-norm optimization framework. Unlike generic L2-norm-based LDA, the proposed algorithm explicitly incorporates the local structure information of sample points and is robust to outliers. It overcomes the problem of the singularity of within-class scatter matrix as well. Experiments on several popular datasets demonstrate the effectiveness of the proposed algorithm.

• 出版日期2018-8
• 单位广东医科大学; 韶关学院; 华南理工大学