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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Vydané v:	Journal of computational and graphical statistics Ročník 28; číslo 1; s. 11 - 22
Hlavní autori:	Molstad, Aaron J., Rothman, Adam J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 02.01.2019 American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundation of North America Taylor & Francis Ltd
Predmet:	Algorithms Alternating minimization algorithm Classification Discriminant analysis Estimators Mathematical models Matrix methods Optimization Optimization with Penalization Penalized likelihood
ISSN:	1061-8600, 1537-2715
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