Abstract:Margin plays an important role in research of machine learning. Margin-based feature selection methods choose the weights of features from the view of classification. This paper analyzes different types of margin and proposed methods to improve the Sequential Backward Selection (SBS) method respectively using sample-margin and hypothesis-margin as feature selection criterion. A SVM polynomial classifier, which has optimal hyper-parameters, is then designed for face recognition. Experiments are conducted on FERET face database. Recognition accuracies between the proposed methods and relief feature selection method are compared. Experiments are also conducted by respectively using SVM and Nearest Neighbor (NN) classifier. Experimental results indicate that the proposed feature selection and recognition methods are efficient for face recognition.
李伟红; 陈伟民; 杨利平; 龚卫国. 基于不同Margin的人脸特征选择及识别方法[J]. 电子与信息学报, 2007, 29(7): 1744-1748 .
Li Wei-hong; Chen Wei-min; Yang Li-ping; Gong Wei-guo. Face Feature Selection and Recognition Based on Different Types of Margin. , 2007, 29(7): 1744-1748 .