PRINCIPAL COMPONENT OUTLIER DETECTION AND SIMCA - A SYNTHESIS

被引：36

作者：

MERTENS, B ^{[1
]}

THOMPSON, M ^{[1
]}

FEARN, T ^{[1
]}

机构：

[1] UNIV LONDON UNIV COLL, DEPT STAT SCI, LONDON WC1E 6BT, ENGLAND

来源：

ANALYST | 1994年 / 119卷 / 12期

关键词：

OUTLIERS; PRINCIPAL COMPONENT; SOFT INDEPENDENT MODELING OF CLASS ANALOGY; PATTERN RECOGNITION; SPECTROSCOPY;

D O I：

10.1039/an9941902777

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页码：2777 / 2784

页数：8

共 33 条

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