On the Semi-Supervised Joint-Trained Elastic Net

• Activity Number: 615
• Type: Invited
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: JCGS-Journal of Computational and Graphical Statistics
• Author(s): Mark V. Culp*+
• Companies: West Virginia University
• Keywords: Semi-supervised Learning ; Joint Optimization ; Elastic Net Regression ; Variable Selection

Abstract

The supervised elastic net is a popular and computationally efficient approach for performing the simultaneous tasks of selecting variables, decorrelation, and shrinking the coefficient vector in the linear regression setting. Both the variable selection and decorrelation components of the supervised elastic net inherently rely on the pairwise correlation structure in the feature data. In circumstances in which the number of variables is high, the feature data is relatively easy to obtain and the response is expensive to generate, it seems reasonable that one would want to be able to use any existing unlabeled observations to more accurately define these correlations. However, the supervised elastic net is not able to incorporate this information and focuses only on the information within the labeled data. In this talk, I will propose and demonstrate that the joint trained elastic net does indeed allow the unlabeled data to influence the variable selection, decorrelation, and shrinkage capabilities of the linear estimator. The joint trained elastic net is demonstrated on a text mining application.