一种新的特征提取方法及其在模式识别中的应用  

New feature extraction method and its application to pattern recognition

作　　者：刘宗礼[1] 曹洁[1] 郝元宏[1] 

机构地区：[1]兰州理工大学计算机与通信学院,兰州730050

出　　处：《计算机应用》2009年第4期1032-1035,共4页journal of Computer Applications

基　　金：甘肃省自然科学基金资助项目(0803RJZA025)

摘　　要：核典型相关分析(KCCA)是一种有监督的机器学习方法,可以有效地提取非线性特征。然而随着训练样本数目的增加,标准的KCCA方法的计算复杂度会随之增加。针对此缺点,提出一种改进的KCCA方法:首先用几何特征选择方法选择一个训练样本子集并将其映射到再生核希尔伯特空间(RKHS),然后设计了一种提升特征提取效率的算法,该算法按照对特征分类贡献的大小巧妙地选取样本的特征值,进而求出其相应的特征向量,最后将改进的KCCA与支持向量数据描述(SVDD)多分类器相结合用于分类识别。在ORL人脸图像数据库上的实验结果表明,改进的方法相对传统的KCCA方法,在不影响识别率的情况下提高了人脸识别速度,减小了系统存储量。Kernel Canonical Correlation Analysis （KCCA） is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features. However, the standard KCCA algorithm may suffer from computational problem as the training set increases. To overcome the drawback, an improved KCCA was proposed. Firstly, a scheme based on geometrical consideration was proposed to select a subset of samples that were projected to feature space （ Reproducing Kernel Hilbert Space）. And then, an efficient algorithm was proposed to enhance the efficiency of the feature extraction, which selected the most contributive eigenvectors for training and classification, and then calculated the corresponding eigenvectors for classification. Finally, the improved KCCA was combined with a multi-class classification method based on Support Vectors Data Description （SVDD） for classification and recognition, which put forward new ideas for pattern recognition based on kernels. The experimental results on ORL face database show that the proposed method reduces the training time and the system storage without deteriorating the recognition accuracy compared with standard KCCA.

关 键 词：人脸识别 核典型相关分析 特征向量选择 支持向量数据描述 

分 类 号：TP391.42[自动化与计算机技术—计算机应用技术]