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Classification with high dimensional features
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DOI：10.1002/wics.1453.png)
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Abstract
En 中文
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
Keywords:
classification
feature selection
penalization
sparsity
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5.4
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201
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