Locality Preserving Fisher Discriminant Analysis with Clustering

摘要：

　　Fisher discriminant analysis (FDA) is an important feature extraction method for many classifiers.However,it tends to give undesired results if samples in some classes form several separate clusters,i.e.,multimodal.This paper proposed a new feature extraction method called locality preserving Fisher discriminant analysis with clustering (LPFDA) for multimodal data.First new classes are formed by clustering data according to labels,then the between-subclass scatter matrix and within-subclass scatter matrix are computed by new classes,finally the vectors are choose which will maximize the Fisher criterion function as the discriminant vector.When our method is applied to the recognition problems of digits and images,and the experimental results show the better performance than the original one.

关键词： Fisher discriminant analysis Locality preserving Feature extraction Clustering

作者: Lishan Zou Yuechao Wang Zhenzhou Chen Xiaorong Wu

作者单位: College of Information and Automobile Engineering,Guangzhou City Polytechnic,Guangzhou, 510405, Chin Computer School South China Normal University Guangzhou, 510631, China School of CISCO Informatics China, Guangdong University of Foreign Studies Guangzhou, 510420, China

会议类型: 国际会议

会议名称: 2012 2nd International Conference on Computer Application and System Modeling(2012第二届计算机应用与系统建模国际会议)(ICCASM-2012)

会议地点: 沈阳

会议语种:英文

页码: 265-268

在线出版日期: 2012-07-27（万方平台首次上网日期，不代表论文的发表时间）