Classification with high dimensional features

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2 Scopus citations

Abstract

Rapid advances in technology have made classification with high dimensional features and ubiquitous problem in modern scientific studies and applications. There are three fundamental goals in the pursuit of a good high-dimensional classifier: accuracy, interpretability, and scalability. In the past 15 years, a host of competitive high-dimensional classifiers have been developed based on sparse regularization techniques. In this article, we give a selective overview of these classification methods. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery.

Original languageEnglish (US)
Article numbere1453
JournalWiley Interdisciplinary Reviews: Computational Statistics
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2019

Bibliographical note

Funding Information:
This work is supported by NSF grant DMS-1505111. The author thanks three referees for their helpful comments and suggestions.

Publisher Copyright:
© 2018 Wiley Periodicals, Inc.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • classification
  • feature selection
  • penalization
  • sparsity

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