Classification with high dimensional features

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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
Issue number1
StatePublished - Jan 1 2019



  • classification
  • feature selection
  • penalization
  • sparsity

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