基于QR分解的扩展监督局部保留映射  

Extended Supervised Locality Preserving Projection Based on QR Decomposition

作　　者：江艳霞[1] 刘子龙[1] 

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《计算机工程》2010年第12期198-199,203,共3页Computer Engineering

基　　金：国家自然科学基金资助项目"不确定非完整运动学控制系统的鲁棒镇定"(60874002)

摘　　要：针对局部保留映射(LPP)算法不能提供数据集的差异信息问题,提出一种基于QR分解的扩展有监督LPP算法。该方法对训练数据矩阵进行QR分解,采用有监督的LPP算法进行降维,利用类别信息对降维后的数据进行Fisher线性判别式分析,得到最终的映射矩阵以提高判别性能。实验结果表明,该方法较主成分分析法和LPP方法有更好的判别性能。Aiming at the problem of Locality Preserving Projection（LPP） does not provide the discriminating information of data set, this paper proposes an algorithm named Extensible Supervised LPP based on QR decomposition（ESLPP/QR）. In the proposed algorithm, a dimension reduction algorithm of supervised locality preserving projection based on QR decomposition of training data matrix, namely SLPP/QR, is developed. It is efficient to solve the under-sampled problem. Using the discriminating information, the obtained SLPP/QR is combined with Fisher linear discriminant to receive final projection matrix and improve discriminant performance. Experimental results show that the algorithm has better discriminant performance than Principal Component Analysis（PCA） and LPP.

关 键 词：主成分分析 局部保留映射 QR分解 Fisher线性判别式 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]