摘要
针对局部线性嵌入算法使用欧氏距离计算非对齐样本相似性时,受数据位置差影响较大,导致度量精度较低,影响算法特征提取精度的问题,提出一种基于信息熵度量的局部线性嵌入算法.首先利用信息熵统计样本特征间的混乱程度,提高划分局部邻域的准确性;然后建立局部重构模型,挖掘出流形的本质结构;最后利用局部结构构建低维重构模型,以获得样本的显著特征.通过在轴承数据集上的实验证明了该算法在特征提取方面的有效性.
Aiming at the problem that when the local linear embedding algorithm used Euclidean distance to calculate the similarity of unaligned samples,it was greatly affected by data position difference,resulting in low measurement accuracy and affecting the accuracy of the algorithm feature extraction,we proposed a local linear embedding algorithm based on information entropy measurement.Firstly,the degree of confusion among sample features was counted by information entropy to improve the accuracy of dividing local neighborhoods.Secondly,a local reconstruction model was established to dig out the essential structure of the manifold.Finally,the local structure was used to build a low-dimensional reconstruction model to obtain the salient features of the samples.The experiment on the bearing data set proves the effectiveness of the algorithm in feature extraction.
作者
刘均
宫子栋
吴力
LIU Jun;GONG Zidong;WU Li(School of Electrical Engineering&Information,Northeast Petroleum University,Daqing 163318,Heilongjiang Province,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2022年第1期143-149,共7页
Journal of Jilin University:Science Edition
基金
黑龙江省自然科学基金(批准号:LH2020F005)
东北石油大学青年科学基金(批准号:2018QNL-33)
东北石油大学研究生创新科研项目(批准号:YJSCX2017-027NEPU).
关键词
局部线性嵌入
特征提取
信息熵
数据对齐
local linear embedding
feature extraction
information entropy
data alignment
