Sparse discriminant analysis

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    Abstract

    We consider the problem of performing interpretable classification in the high-dimensional setting, in which the number of features is very large and the number of observations is limited. This setting has been studied extensively in the chemometrics literature, and more recently has become commonplace in biological and medical applications. In this setting, a traditional approach involves performing feature selection before classification. We propose sparse discriminant analysis, a method for performing linear discriminant analysis with a sparseness criterion imposed such that classification and feature selection are performed simultaneously. Sparse discriminant analysis is based on the optimal scoring interpretation of linear discriminant analysis, and can be extended to perform sparse discrimination via mixtures of Gaussians if boundaries between classes are nonlinear or if subgroups are present within each class. Our proposal also provides low-dimensional views of the discriminative directions. © 2011 American Statistical Association and the American Society for Qualitys.
    Original languageEnglish
    JournalTechnometrics
    Volume53
    Issue number4
    Pages (from-to)406-413
    ISSN0040-1706
    DOIs
    Publication statusPublished - 2011

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