L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso. (July 2016)

• Record Type: Journal Article
• Authors: Lu, Gui-Fu; Zou, Jian; Wang, Yong
• Is Part Of: Pattern recognition. Volume 55(2016:Jul.)
• Page Start: 207
• Page End: 214
• Publication Date: 2016-07
• Subjects: Discriminant locality preserving projections -- Feature extraction -- Maximum margin criterion -- L1-norm -- Trace Lasso -- L2-norm; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.01.029
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Discriminant locality preserving projections based on maximum margin criterion (DLPP/MMC) is a useful feature extraction method since it has shown good performances in pattern recognition. The conventional DLPP/MMC, however, is not robust to noises and outliers since its objective function is based on L2-norm. In this paper, we propose a novel L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso (DLPP/MMC-L1TL). L1-norm rather than L2-norm is used in the formulation of DLPP/MMC-L1TL, which makes it be robust to noises and outliers. Besides, in order to improve the performance of DLPP/MMC-L1TL further, we use trace Lasso to regularize the basis vectors. Trace Lasso, which can balance L1-norm and L2-norm and consider sparsity and correlation of data simultaneously, is a recently proposed norm. An iterative procedure for solving DLPP/MMC-L1TL is also proposed in this paper. The experiment results on some data sets demonstrate the effectiveness of DLPP/MMC-L1TL. Highlights: We propose a L1-norm and MMC based DLPP method via trace Lasso. Our proposed algorithm can simultaneously consider the sparsity and correlation. We also propose an efficient procedure for solving the proposed method.