基于自适应邻域及重构权重的局部线性嵌入算法  

Local Linear Embedding Algorithm Based on Adaptive Neighborhood and Reconstruction Weight

作　　者：梁磊 刘远红[1] 甘智峰 LIANG Lei;LIU Yuanhong;GAN Zhifeng(School of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China;Digital Operation and Maintenance Center,No.10 Oil Production Plant,Daqing Oilfield Company Limited,Daqing 163318,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318 [2]大庆油田有限责任公司第十采油厂数字化运维中心,黑龙江大庆163318

出　　处：《吉林大学学报(信息科学版)》2024年第6期1031-1040,共10页Journal of Jilin University(Information Science Edition)

基　　金：海南省自然科学基金资助项目(623MS071)。

摘　　要：针对局部线性嵌入(LLE:Locally Linear Embedding)算法邻域选择不精确及度量方法缺陷导致不能提取流形真实结构的问题,提出一种基于自适应邻域及重构权重的局部线性嵌入算法(AN-RWLLE:Locally Linear Embedding Algorithm Based on Adaptive Neighborhood and Reconstruction Weight)。首先,通过计算高维样本点的余弦相似性,筛选出每个样本点的局部邻域,再从该邻域中自适应选择最优邻域。其次,融合最优邻域内样本点的距离和结构特征,充分挖掘高维数据流形结构,实现权重重构。最后,利用支持矢量机对特征进行识别,在低维空间保持高维数据的本质特征。实验结果表明,AN-RWLLE算法具有很好的可视化和聚类效果,在两组轴承故障数据集上都具有很好的特征提取能力。In response to the issues of inaccurate neighborhood selection and deficiencies in the metric used in the LLE(Locally Linear Embedding)algorithm,which hinder its ability to extract the true manifold structure,an algorithm called AN-RWLLE(Locally Linear Embedding Algorithm Based on Adaptive Neighborhood and Reconstruction Weight)is proposed.Firstly,the local neighborhoods of each sample point are identified by calculating the cosine similarity of high-dimensional sample points,followed by an adaptive selection of the optimal neighborhood within those neighborhoods.Secondly,the distance features and structural features of the sample points within the optimal neighborhood are combined to thoroughly explore the manifold structure of high-dimensional data and achieve weight reconstruction.Lastly,support vector machines are employed for feature recognition,preserving the intrinsic characteristics of high-dimensional data in a lower-dimensional space.Experimental results demonstrate that the AN-RWLLE algorithm exhibits excellent visualization,clustering performance,and effective feature extraction capabilities on two sets of bearing fault datasets.

关 键 词：局部线性嵌入 特征提取 自适应邻域 重构权重 轴承故障诊断 

分 类 号：TN911.23[电子电信—通信与信息系统]