基于类内子空间学习的局部线性嵌入算法  

Local linear embedding algorithm based on intra-class subspace learning

作　　者：王程锦 王秀友[2] 林玉娥[1] WANG Chengjin;WANG Xiuyou;LIN Yu’e(School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan Anhui 232000,China;School of Computer and Information Engineering,Fuyang Normal University,Fuyang Anhui 236037,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232000 [2]阜阳师范学院计算机与信息工程学院,安徽阜阳236037

出　　处：《阜阳师范学院学报（自然科学版）》2019年第2期52-56,共5页Journal of Fuyang Normal University(Natural Science)

基　　金：安徽省高校自然科学研究重点项目(KJ2016A556,KJ2016A554)资助

摘　　要：为了充分利用样本的类别信息,提取出更加有效的分类特征,提出一种基于类内子空间学习的局部线性嵌入算法。该算法首先获取类内离散矩阵的子空间,然后采用类内子空间构成类间离散矩阵;为了进一步增强算法的性能,使用了结合最大边界准则与局部线性嵌入算法的目标函数。最后在人脸数据库上的实验结果表明,同其他算法相比,ISL/LLE算法具有更好的识别性能。In order to make full use of the class information of the sample and extract more effective classification features,a local linear embedding algorithm based on intra-class subspace learning is proposed.The algorithm first obtains the subspace of the intra-class discrete matrix,and then uses the intra-class subspace to form the inter-class discrete matrix.To further enhance the performance of the algorithm,the objective function combining the maximum margin criterion and the locally linear embedding is utilized.Finally,the experimental results on the face database show that the ISL/LLE algorithm has better recognition performance than other algorithms.

关 键 词：最大边界准则 类内子空间 局部线性嵌入 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]