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.
作者
邱建荣
罗汉
QIU Jianrong;LUO Han(School of Mathematics and Econometric,Hunan University,Changsha 410082,China)
出处
《计算机工程与应用》
CSCD
北大核心
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）
关键词
局部线性嵌入
流形学习
降维
GRDLLE算法
locally linear embedding
manifold learning
dimensionality reduction
GRDLLE algorithm
