PRINCIPAL COMPONENT OUTLIER DETECTION AND SIMCA - A SYNTHESIS.
2777 - 2784.
Principal component outlier detection methods are discussed and their application in the soft independent modelling of class analogy (SIMCA) method of pattern recognition is clarified. SIMCA is compared to allocation procedures based on the Mahalanobis distance. Finally, the differences between the SIMCA method and quadratic discriminant analysis are discussed. The discussion is illustrated with an example from spectroscopy.
|Title:||PRINCIPAL COMPONENT OUTLIER DETECTION AND SIMCA - A SYNTHESIS|
|Keywords:||OUTLIERS, PRINCIPAL COMPONENT, SOFT INDEPENDENT MODELING OF CLASS ANALOGY, PATTERN RECOGNITION, SPECTROSCOPY, NEAR-INFRARED SPECTRA, DISCRIMINANT-ANALYSIS, DESIGNED EXPERIMENT, MODELS, RESIDUALS|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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