因子分析判别准则的线性降维方法研究  

Linear dimension reduction by factors analysis discriminant criterion

作　　者：巩知乐[1] 张少龙[1] 廖海斌[2] 

机构地区：[1]平顶山工业职业技术学院计算机与软件工程学院,河南平顶山467000 [2]湖北科技学院计算机科学与技术学院,湖北咸宁437100

出　　处：《计算机工程与应用》2016年第3期145-149,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61271256);河南省重大科技攻关项目(No.072SGZS38042);湖北科技学院博士启动基金资助项目(No.BK1418)

摘　　要：提取稳定且具有判别性的低维特征是模式识别研究中的关键问题。在深入研究Fisher判别准则的基础上,从因子分析的实际角度考虑,提出基于因子分析的判别准则,解决Fisher判别准则类内和类间散布矩阵非最优定义问题。通过在合成数据集和真实人脸数据集上进行实验比较表明,该方法在解决数据集中的边缘类和人脸的表情、姿态变化等问题上比Fisher判别准则更优。The stability and discrimination of low-dimensional feature extracting are the key problems in the study of the pattern recognition. In terms of the practical application, this paper puts forward the discriminate criterion based on factors analysis and the in-depth study of Fisher discriminate criterion. It aims to solve the problem of the suboptimal definition for Fisher criterion with between-class scatter matrix and within-class scatter matrix. The experimental comparison between the synthetic data and real face data sets demonstrates that the presented algorithm possesses better robustness to deal with the data centralized edge and facial variations of expressions or poses than Fisher discriminate criterion.

关 键 词：特征提取 线性降维 FISHER判别准则 因子分析 人脸识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]