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Simple and interpretable discrimination

Trendafilov, N. T. and Vines, K. (2009). Simple and interpretable discrimination. Computational Statistics and Data Analysis, 53(4) pp. 979–989.

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A number of approaches have been proposed for constructing alternatives to principal components that are more easily interpretable, while still explaining considerable part of the data variability. One such approach is employed in order to produce interpretable canonical variates and explore their discrimination behavior, which is more complicated as orthogonality with respect to the within-groups sums-of-squares matrix is involved. The proposed simple and interpretable canonical variates are an optimal choice between good and sparse approximation to the original ones, rather than identifying the variables that dominate the discrimination. The numerical algorithms require low computational cost, and are illustrated on the Fisher’s iris data and on moderately large real data.

Item Type: Journal Item
Copyright Holders: 2009 Elsevier B.V
ISSN: 0167-9473
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 20216
Depositing User: Sarah Frain
Date Deposited: 10 Mar 2010 14:42
Last Modified: 07 Dec 2018 09:32
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