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Domain Based LDA and QDA

Authors: Piotr Juszczak; David M. J. Tax; Serguei Verzakov; Robert P. W. Duin;

Domain Based LDA and QDA

Abstract

We propose an alternative to probability density classifiers based on normal distributions LDA and QDA. Instead of estimating covariance matrices using the standard maximum likelihood estimator we estimate class domains by the minimum volume enclosing ellipsoid (i-MVEE). The i-MVEE is a robust statistic rejecting a specified fraction i of the data. The performance of the domain and density approaches are compared in small sample size problems and in situations where sampling of a training and test sets is not i.i.d..

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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