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Functional Robust Support Vector Machines for Sparse and Irregular Longitudinal Data
Functional and longitudinal data are becoming more and more common in practice. This article focuses on sparse and irregular longitudinal data with a multicategory response. The predictor consists of sparse and irregular observations, potentially contaminated with measurement errors, on the predictor trajectory. To deal with this type of complicated predictor, we borrow the strength of large-margin classifiers in statistical learning for classification of sparse and irregular longitudinal data. In particular, we propose functional robust truncated-hinge-loss support vector machines to perform multicategory classification with the aid of functional principal component analysis. This article has online supplementary material.
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