Semi-supervised Discriminant Analysis method Via Weighted Low-rank Representation and Adaptive Neighbor Selection
Dimensionality reduction is a very important part in the field of face recognition.In view of the problem of the traditional dimensionality reduction methods are inconvenient to select neighbor parameter K and the dense characteristic of the low-rank representation coefficient matrix.We presented a method that semi-supervised discriminant analysis via weighted low-rank representation and adaptive neighbor selection(ANSWLR-SDA).First,we uses all the within-class samples to construct the within-class graph which can describe the within-class compactness,and then adaptively chooses the between-class samples to construct the between-class graph which can describe the between-class respectively.On this basis,we use a regularization term by weighted low-rank represented to maintain the global similarity structure of samples.Finally,we carry out the experiments on FERET and yale_faceb databases,and compare this method with the traditional dimensionality reduction methods and the results demonstrate that ANSWLR-SDA method is effectiveness and robust to different types of noise than other state-of-art face recognition method.
dimensionality reduction semi-supervised weighted low-rank adaptively face recognition
Xue Zhou Zhengqun Wang Zhibo Guo Chen Yan Dongling Zhai
School of Information Engineering,Yangzhou University,Yangzhou,China
国际会议
重庆
英文
987-991
2017-03-25（万方平台首次上网日期，不代表论文的发表时间）