Gaussian process latent variable models for fault detection

被引：1

作者：

Eciolaza, Luka ^{[1
]}

Alkarouri, A. ^{[1
]}

Lawrence, N. D. ^{[2
]}

Kadirkamanathan, V. ^{[1
]}

Fleming, P. J. ^{[1
]}

机构：

[1] Univ Sheffield, Rolls Royce Supported Univ Technol Ctr Control &, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

[2] Univ Sheffield, Dept Comp Sci, Sheffield S10 2TN, S Yorkshire, England

来源：

2007 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING, VOLS 1 AND 2 | 2007年

关键词：

fault detection; dimensionality reduction; principal component analysis;

D O I：

10.1109/CIDM.2007.368886

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：287 / 292

页数：6

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