Composite Large-Margin Classifiers with Latent Subclasses

• Activity Number: 368
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Guanhua Chen*+ and Yufeng Liu and Michael R. Kosorok
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina and The University of North Carolina-Chapel Hill
• Keywords: Classification ; High dimensional data ; Large margin ; Latent subclasses ; Principle component regression

Abstract

High dimensional classification problems are prevalent in a wide range of modern scientific applications. Despite a large number of candidate classification techniques available to use, practitioners often face a dilemma of the choice between linear and general nonlinear classifiers. Specifically, simple linear classifiers have good interpretability, but may have limitations in handling data with complex structures. In contrast, general nonlinear kernel classifiers are more flexible but may lose interpretability and have more tendency of overfitting. In this paper, we consider data with potential latent subgroups in the classes of interest. We propose a new group of Composite Large Margin Classifier (CLM) to address the issue of classification with latent subclasses. The CLM aims to find three linear functions simultaneously: one linear function to split the data into two parts, with each part being classified by a different linear classifier. Our method has comparable prediction accuracy to a general nonlinear kernel classifier without overfitting the training data, at the same time maintaining the interpretability of traditional linear classifiers.