改进的局部线性嵌入算法及其应用  

Improved Local Linear Embedding Algorithm and Its Application

作　　者：邱建荣 罗汉[1] QIU Jianrong;LUO Han(School of Mathematics and Econometric,Hunan University,Changsha 410082,China)

机构地区：[1]湖南大学数学与计量经济学院

出　　处：《计算机工程与应用》2020年第3期176-179,共4页Computer Engineering and Applications

基　　金：国家自然科学基金（No.11571100）

摘　　要：局部线性嵌入算法(LLE)中常用欧氏距离来度量样本间相似度,而对于具有低维流形结构的高维数据,欧氏距离不能衡量流形上两点间相对位置关系。提出基于Geodesic Rank-order距离的局部线性嵌入算法(简称GRDLLE)。应用最短路径算法(Dijkstra算法)找到最短路径长度来近似计算任意两个样本间的测地线距离,计算Rank-order距离用于LLE算法的相似性度量。将GRDLLE算法、其他改进LLE的流形学习算法及2DPCA算法在ORL与Yale数据集上进行对比实验,对数据用GRDLLE算法进行降维后人脸识别率有所提高,结果表明GRDLLE算法具有很好的降维效果。Euclidean distance is normally used to measure the similarity between samples in Localiy Linear Embedding algorithm(LLE),But for some high dimensional data with low-dimensional manifold structure,Euclidean distance does not measure the relative position of two points in a manifold.A Local Linear Embedding algorithm based on Geodesic Rank-order Distance(GRDLLE)is proposed.Firstly,the algorithm approximates the geodesic distance between any two sample points by using the shortest path length to find the shortest path algorithm(Dijkstra algorithm).Then the Rank-order distance is calculated for the similarity measurement of the LLE algorithm.GRDLLE,other improved LLE mani-fold learning algorithms and 2DPCA algorithm are compared on ORL and Yale data sets.The face recognition rate of data is improved after dimension-reduction using GRDLLE algorithm.The results show that the GRDLLE algorithm has good dimensional reduction effect.

关 键 词：局部线性嵌入 流形学习 降维 GRDLLE算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]