基于类别信息的监督局部保持投影方法  

Supervised locality preserving projection based on class information

作　　者：李晓曼[1] 王靖[1] 

机构地区：[1]华侨大学计算机科学与技术学院,福建厦门361000

出　　处：《计算机应用》2012年第2期531-534,共4页journal of Computer Applications

基　　金：国家自然科学基金青年基金资助项目(10901062);福建省自然科学基金资助项目(2010J01136)

摘　　要：局部保持投影算法(LPP)是拉普拉斯映射(LE)的线性近似,但LPP作为一种无监督方法,并没有有效利用已有的类别信息提高分类效率。为此提出一种基于类别信息的监督局部保持投影方法(SLPP-LI)。在学习投影矩阵时,SLPP-LI综合利用了流形的几何结构和已有训练点的类别信息,通过调整控制参数的取值,有效地利用已知的低维信息,并且直接求解线性方程获得高维数据的低维模型。通过在多个人脸数据库和手写数字库上的对比实验,表明了SLPP-LI对于高维数据的初始维数以及训练数据的数目并不敏感,与主分量分析法(PCA)、LPP、正交LPP(OLPP)、有监督的LPP(SLPP)相比,均具有较高的识别率,充分说明SLPP-LI算法能够有效处理分类问题。Locality Preserving Projection （LPP） is an approximation of Laplacian Eigenmap （LE）, but it is an unsupervised method, and does not take advantage of the existing classification information to improve the classification efficiency. Therefore, a supervised locality preserving projection named SLPP-LI method was proposed based on class information. In the study of projection matrix, SLPP-LI took advantage of the comprehensive utilization of the geometrical structure of the manifold and the class information of the existing train set, SLPP-LI can effectively take advantage of the known low dimensional information by adjusting the control parameters and obtain the low-dimensional models of high dimensional data by directly solving the linear equation. The comparative experiments with several face databases and handwritten digital databases show, SLPP-LI is neither sensitive to the original dimension of high dimension data, nor the number of the training data. For the same kind of problems, SLPP-LI has higher recognition rate compared with PCA, LPP, OLPP and SLPP, and it can effectively deal with the classification issues.

关 键 词：监督 局部保持投影 类别信息 线性 训练数据 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]