机械科学与技术2025，Vol.44Issue(11)：1904-1911,8.DOI:10.13433/j.cnki.1003-8728.20230340
一种样本密度自适应的局部增强线性嵌入算法
Local Enhanced Linear Embedding Algorithm with Sample Density Adaptation
摘要
Abstract
The local linear embedding(LLE)algorithm needs to manually specify the number of nearest neighbors when mining the local structure of high-dimensional space,which cannot guarantee the algorithm's feature extraction ability.To address this issue,a local enhanced linear embedding algorithm with sample density adaptation(SDA-LELE)is proposed.Firstly,the sum of distances between the sample points and their nearest neighbors is used to measure the sparsity and density of the sample distribution,thereby adaptively selecting the number of nearest neighbors.Secondly,a local enhancement algorithm is adopted to increase the weight between the adjacent samples,so that the samples maintain both local linear structure and local nearest neighbor structure,enhancing the algorithm's feature extraction ability.Finally,the algorithm is applied to the bearing data sets of Case Western Reserve University and Northeast Petroleum University,and visualization,Fisher information and other experiments are carried out.The experimental results show that the SDA-LELE algorithm can extract more significant features and achieve better dimensionality reduction compared to other algorithms.关键词
流形学习/局部线性嵌入/自适应邻域/特征提取/轴承诊断Key words
manifold learning/local linear embedding/adaptive neighborhood/feature extraction/bearing diagnosis分类
矿业与冶金引用本文复制引用
贾凯巍,刘庆强..一种样本密度自适应的局部增强线性嵌入算法[J].机械科学与技术,2025,44(11):1904-1911,8.基金项目
海南省自然科学基金项目(623MS071) （623MS071）
