A Penalized Likelihood Method for Classification With Matrix-Valued Predictors

We propose a penalized likelihood method to fit the linear discriminant analysis model when the predictor is matrix valued. We simultaneously estimate the means and the precision matrix, which we assume has a Kronecker product decomposition. Our penalties encourage pairs of response category mean ma...

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Bibliographic Details

Published in:	Journal of computational and graphical statistics Vol. 28; no. 1; pp. 11 - 22
Main Authors:	Molstad, Aaron J., Rothman, Adam J.
Format:	Journal Article
Language:	English
Published:	Alexandria Taylor & Francis 02.01.2019 American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundation of North America Taylor & Francis Ltd
Subjects:	Algorithms Alternating minimization algorithm Classification Discriminant analysis Estimators Mathematical models Matrix methods Optimization Optimization with Penalization Penalized likelihood
ISSN:	1061-8600, 1537-2715
Online Access:	Get full text
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