Research of Adaptive Neighborhood Incremental Principal Component Analysis and Locality Preserving Projection Manifold Learning Algorithm

作     者：DENG Shijie TANG Liwei ZHANG Xiaotao 邓士杰;唐力伟;张晓涛

作者机构：Department of Artillery Engineering Army Engineering University Shijiazhuang 050003 China 

基　　金：the National Natural Science Foundation of China(No.50775219) 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2018年第23卷第2期

页      码：269-275页

摘      要：In view of the incremental learning problem of manifold learning algorithm, an adaptive neighborhood incremental principal component analysis(PCA) and locality preserving projection(LPP) manifold learning algorithm is presented, and the incremental learning principle of algorithm is introduced. For incremental sample data, the adjacency and covariance matrices are incrementally updated by the existing samples; then the dimensionality reduction results of the incremental samples are estimated by the dimensionality reduction results of the existing samples; finally, the dimensionality reduction results of the incremental and existing samples are updated by subspace iteration method. The adaptive neighborhood incremental PCA-LPP manifold learning algorithm is applied to processing of gearbox fault signals. The dimensionality reduction results by incremental learning have very small error, compared with those by batch learning. Spatial aggregation of the incremental samples is basically stable, and fault identification rate is increased.

主 题 词：incremental learning adaptive manifold learning fault diagnosis 

学科分类：11[军事学] 0810[工学-土木类] 1105[1105] 080202[080202] 08[工学] 0802[工学-机械学] 081002[081002] 110503[110503] 

核心收录：

D　O　I：10.1007/s12204-018-1936-7

馆 藏 号：203286523...