An empirical study of two typical locality preserving linear discriminant analysis methods

: Laplacian Linear Discriminant Analysis (LapLDA) and Semi-supervised Discriminant Analysis (SDA) are two recently proposed LDA methods. They are developed independently with the aim to improve LDA by introducing a locality preserving regularization term, and have been shown their effectiveness experimentally on some benchmark datasets. However, both algorithms ignored comparison with much simpler methods such as Regularized Discriminant Analysis (RDA). In this paper, we make an empirical and supplemental study on LapLDA and SDA, and get somewhat counterintuitive results: 1) Although LapLDA can generally improve the classical LDA via resorting to a complex regularization term, it does not outperform RDA which is only based on the simplest Tikhonov regularizer; 2) To reevaluate the performance of SDA, we develop purposely a new and much simpler semi-supervised algorithm called Globality Preserving Discriminant Analysis (GPDA) and make a comparison with SDA. Surprisingly, we find that GP...

• Type: Journal
• Year: 2010
• Where: IJON
• Authors: Lishan Qiao, Limei Zhang, Songcan Chen
• Keywords: Discriminant Analysis, IJON 2010, Linear Discriminant Analysis, Regularization Term