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February 2020 Prediction and estimation consistency of sparse multi-class penalized optimal scoring
Irina Gaynanova
Bernoulli 26(1): 286-322 (February 2020). DOI: 10.3150/19-BEJ1126

Abstract

Sparse linear discriminant analysis via penalized optimal scoring is a successful tool for classification in high-dimensional settings. While the variable selection consistency of sparse optimal scoring has been established, the corresponding prediction and estimation consistency results have been lacking. We bridge this gap by providing probabilistic bounds on out-of-sample prediction error and estimation error of multi-class penalized optimal scoring allowing for diverging number of classes.

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Irina Gaynanova. "Prediction and estimation consistency of sparse multi-class penalized optimal scoring." Bernoulli 26 (1) 286 - 322, February 2020. https://doi.org/10.3150/19-BEJ1126

Information

Received: 1 September 2018; Revised: 1 March 2019; Published: February 2020
First available in Project Euclid: 26 November 2019

zbMATH: 07140500
MathSciNet: MR4036035
Digital Object Identifier: 10.3150/19-BEJ1126

Keywords: ‎classification‎ , high-dimensional regression , Lasso , linear discriminant analysis

Rights: Copyright © 2020 Bernoulli Society for Mathematical Statistics and Probability

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Vol.26 • No. 1 • February 2020
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