Gaussian Process Latent Variable Models for Fault Detection

Luka Eciolaza, M. Alkarouri, Neil D. Lawrence, Visakan Kadirkamanathan, Peter J. Fleming
Computational Intelligence and Data Mining:287-292, 2007.

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

Cite this Paper

BibTeX
@InProceedings{Eciolaza:fault07, title = {{G}aussian Process Latent Variable Models for Fault Detection}, author = {Eciolaza, Luka and Alkarouri, M. and Lawrence, Neil D. and Kadirkamanathan, Visakan and Fleming, Peter J.}, booktitle = {Computational Intelligence and Data Mining}, pages = {287--292}, year = {2007}, doi = {10.1109/CIDM.2007.368886}, 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} }
Endnote
%0 Conference Paper %T Gaussian Process Latent Variable Models for Fault Detection %A Luka Eciolaza %A M. Alkarouri %A Neil D. Lawrence %A Visakan Kadirkamanathan %A Peter J. Fleming %B Computational Intelligence and Data Mining %D 2007 %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
RIS
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 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 -
APA
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 doi:10.1109/CIDM.2007.368886 Available from http://inverseprobability.com/publications/eciolaza-fault07.html.

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