Gaussian Process Latent Variable Models for Fault Detection

[edit]

Luka Eciolaza
M. Alkarouri
Neil D. Lawrence, University of Sheffield
Visakan Kadirkamanathan
Peter J. Fleming

in Computational Intelligence and Data Mining, pp 287-292

Related Material

Abstract

The Gaussian process latent variable model (GPLVM) is a novel unsupervised approach to nonlinear low dimensional embedding proposed by Lawrence (2005). This paper presents the development of a framework for the implementation of the GPLVM for fault detection. A series of experiments have been carried out comparing and combining the GPLVM to the conventional and widely used linear dimension reduction technique of principal component analysis (PCA). The inclusion of the GPLVM for the visualisation and data analysis, led to a considerable improvement in the classification results


@InProceedings{eciolaza-fault07,
  title = 	 {Gaussian Process Latent Variable Models for Fault Detection},
  author = 	 {Luka Eciolaza and M. Alkarouri and Neil D. Lawrence and Visakan Kadirkamanathan and Peter J. Fleming},
  booktitle = 	 {Computational Intelligence and Data Mining},
  pages = 	 {287},
  year = 	 {2007},
  month = 	 {00},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2007-01-01-eciolaza-fault07.md},
  url =  	 {http://inverseprobability.com/publications/eciolaza-fault07.html},
  abstract = 	 {The Gaussian process latent variable model (GPLVM) is a novel unsupervised approach to nonlinear low dimensional embedding proposed by Lawrence (2005). This paper presents the development of a framework for the implementation of the GPLVM for fault detection. A series of experiments have been carried out comparing and combining the GPLVM to the conventional and widely used linear dimension reduction technique of principal component analysis (PCA). The inclusion of the GPLVM for the visualisation and data analysis, led to a considerable improvement in the classification results},
  key = 	 {Eciolaza:fault07},
  doi = 	 {10.1109/CIDM.2007.368886},
  group = 	 {gplvm}
 

}
%T Gaussian Process Latent Variable Models for Fault Detection
%A Luka Eciolaza and M. Alkarouri and Neil D. Lawrence and Visakan Kadirkamanathan and Peter J. Fleming
%B 
%C Computational Intelligence and Data Mining
%D 
%F eciolaza-fault07	
%P 287--292
%R 10.1109/CIDM.2007.368886
%U http://inverseprobability.com/publications/eciolaza-fault07.html
%X The Gaussian process latent variable model (GPLVM) is a novel unsupervised approach to nonlinear low dimensional embedding proposed by Lawrence (2005). This paper presents the development of a framework for the implementation of the GPLVM for fault detection. A series of experiments have been carried out comparing and combining the GPLVM to the conventional and widely used linear dimension reduction technique of principal component analysis (PCA). The inclusion of the GPLVM for the visualisation and data analysis, led to a considerable improvement in the classification results
TY  - CPAPER
TI  - Gaussian Process Latent Variable Models for Fault Detection
AU  - Luka Eciolaza
AU  - M. Alkarouri
AU  - Neil D. Lawrence
AU  - Visakan Kadirkamanathan
AU  - Peter J. Fleming
BT  - Computational Intelligence and Data Mining
PY  - 2007/01/01
DA  - 2007/01/01	
ID  - eciolaza-fault07	
SP  - 287
EP  - 292
DO  - 10.1109/CIDM.2007.368886
UR  - http://inverseprobability.com/publications/eciolaza-fault07.html
AB  - The Gaussian process latent variable model (GPLVM) is a novel unsupervised approach to nonlinear low dimensional embedding proposed by Lawrence (2005). This paper presents the development of a framework for the implementation of the GPLVM for fault detection. A series of experiments have been carried out comparing and combining the GPLVM to the conventional and widely used linear dimension reduction technique of principal component analysis (PCA). The inclusion of the GPLVM for the visualisation and data analysis, led to a considerable improvement in the classification results
ER  -

Eciolaza, L., Alkarouri, M., Lawrence, N.D., Kadirkamanathan, V. & Fleming, P.J.. (2007). Gaussian Process Latent Variable Models for Fault Detection. Computational Intelligence and Data Mining :287-292