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A Hybrid Methodology For On-Line Process Monitoring
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May 6, 2004
A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-à-vis current practice.
Keywords: Process monitoring; locally linear embedding; abnormality detection; support vector domain distribution; novelty detection; single class classification
Published Online: 2004-5-6
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Keywords for this article
Process monitoring;
locally linear embedding;
abnormality detection;
support vector domain distribution;
novelty detection;
single class classification
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