sa ›› 2018, Vol. 23 ›› Issue (2): 269-275.doi: 10.1007/s12204-018-1936-7

• • 上一篇    下一篇

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

DENG Shijie (邓士杰), TANG Liwei (唐力伟), ZHANG Xiaotao (张晓涛)   

  1. (Department of Artillery Engineering, Army Engineering University, Shijiazhuang 050003, China)
  • 出版日期:2018-04-01 发布日期:2018-06-19
  • 通讯作者: DENG Shijie (邓士杰) E-mail:dsj sjz@163.com

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

DENG Shijie (邓士杰), TANG Liwei (唐力伟), ZHANG Xiaotao (张晓涛)   

  1. (Department of Artillery Engineering, Army Engineering University, Shijiazhuang 050003, China)
  • Online:2018-04-01 Published:2018-06-19
  • Contact: DENG Shijie (邓士杰) E-mail:dsj sjz@163.com

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.

关键词: incremental learning, adaptive, manifold learning, fault diagnosis

Abstract: 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.

Key words: incremental learning, adaptive, manifold learning, fault diagnosis

中图分类号: