Functional Robust Support Vector Machines for Sparse and Irregular Longitudinal Data

• Activity Number: 307
• Type: Invited
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: JCGS-Journal of Computational and Graphical Statistics
• Author(s): Yichao Wu*+ and Yufeng Liu
• Companies: North Carolina State University and The University of North Carolina at Chapel Hill
• Keywords: classification ; functional principal component analysis ; longitudinal data ; multicategory ; sparse and irregular ; truncated-hinge-loss SVM

Abstract

This paper 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 predictors, 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.