Classifiability-based Optimal Discriminatory Projection Pursuit

Linear Discriminant Analysis (LDA) might be the most widely used linear feature extraction method in pattern recognition. Based on the analysis on the several limitations of traditional LDA, this paper makes an effort to propose a new computational paradigm named Optimal Discriminatory Projection Pursuit (ODPP), which is totally different from the traditional LDA and its variants. Only two simple steps are involved in the proposed ODPP: one is the construction of candidate projection set; the other is the optimal discriminatory projection pursuit. For the former step, candidate projections are generated as the difference vectors between nearest between-class boundary samples with redundancy well-controlled, while the latter is efficiently achieved by classifiability-based AdaBoost learning from the large candidate projection set. We show that the new 'projection pursuit' paradigm not only does not suffer from the limitations of the traditional LDA but also inherits good generalizability from the boundary attribute of candidate projections. Extensive experimental comparisons with LDA and its variants on synthetic and real data sets show that the proposed method consistently has better performances. ?2008 IEEE.EI

Face Recognition By Stepwise Nonparametric Margin Maximum Criterion

Linear Discriminant Analysis (LDA) is a popular feature extraction technique in face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a new nonparametric linear feature extraction method, stepwise nonparametric margin maximum criterion(SNMMC), is proposed to find the most discriminant directions, which does not assume that the class densities belong to any particular parametric family and does not depend on the nonsingularity of the within-class scatter matrix either. On three datasets from ATT and FERET face databases, our experimental results demonstrate that SNMMC outperforms other methods and is robust to variations of pose, illumination and expression