基于核对称散布矩阵空间的特征抽取方法  

Feature Extraction Approach Based on Kernel Symmetrical Scatter Matrix Space

作　　者：段旭[1,2] 林庆[1] 高尚[2] 

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013 [2]江苏科技大学计算机科学与工程学院,江苏镇江212003

出　　处：《计算机工程》2011年第10期165-166,169,共3页Computer Engineering

基　　金：江苏省高校自然科学基金资助项目(08KJB520003)

摘　　要：为解决传统Fisher鉴别分析方法中非线性小样本的特征抽取问题,从核线性子空间角度出发,构造一种矩阵变换,得到核空间中类内散布矩阵的另一个对称核子空间,通过对2个核子空间分别求解,从而得到样本的有效鉴别信息。在NUST603和ORL人脸数据库上的实验结果验证了该算法的有效性。In order to solve the feature extraction problem of nonlinear small sample sizes present in the traditional Fisher discriminant analysis method,a matrix transform is proposed on the basis of kernel linear subspace theory,by which a new kernel symmetrical linear subspace of within-class scatter matrix is constructed.Two kernel solution spaces derived from the within-class scatter matrix and its corresponding symmetrical subspace are respectively utilized to obtain the efficient discriminatory information of the samples.Experimental results conduct on the NUST603 and ORL face databases demonstrate the effectiveness of the proposed method.

关 键 词：特征抽取 线性鉴别分析 对称子空间 小样本问题 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]