Issue 12, 1994

Principal component outlier detection and SIMCA: a synthesis

Abstract

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.

Article information

Article type
Paper

Analyst, 1994,119, 2777-2784

Principal component outlier detection and SIMCA: a synthesis

B. Mertens, M. Thompson and T. Fearn, Analyst, 1994, 119, 2777 DOI: 10.1039/AN9941902777

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements