Some practical aspects of covariance estimation

Vectorcardiogram (VCG) data often are analyzed using the Karhunen-Loéve expansion of the sample covariance matrix, S, as a method for discriminating between the VCG's of healthy pat unhealthy patients. The estimator, S, however, can be seriously effected by both atypical observations and the nu...

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Bibliographic Details
Published in:Pattern recognition letters 1985-01, Vol.3 (5), p.295-298
Main Authors: Keating, Jerome P, Mason, Robert L
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
discrimination
Exact sciences and technology
Karhunen-Loéve expansion
Outliers
Pattern recognition. Digital image processing. Computational geometry
robust estimators
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Summary:Vectorcardiogram (VCG) data often are analyzed using the Karhunen-Loéve expansion of the sample covariance matrix, S, as a method for discriminating between the VCG's of healthy pat unhealthy patients. The estimator, S, however, can be seriously effected by both atypical observations and the number of VCG's in the database relative to their dimension. In this paper it is shown that alternative robust estimators of the covariance matrix are appealing in analyzing VCG data when outliers are present in the sample. Also, it is demonstrated that sample sizes in such experiments should be greatly expanded in order to validate the asymptotic properties of S.
ISSN:0167-8655
1872-7344
DOI:10.1016/0167-8655(85)90058-3