On Propagated Scoring for Semi-Superivsed Additive Models

• Activity Number: 646
• Type: Topic Contributed
• Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Computing
• Author(s): Mark Culp*+
• Companies: WVU
• Keywords: learning ; semi-supervised ; smoothing ; text data ; protien data ; network

Abstract

In this talk, a semi-supervised modeling framework that combines feature-based (x) data and graph-based (G) data for classification/regression of the response (Y) is presented. In this semi-supervised setting, Y is observed for a subset of the observations (labeled) and missing for the remainder (unlabeled). The Propagated Scoring algorithm proposed for fitting this model is a semi-supervised fixed point regularization approach that essentially extends the generalized additive model into the semi-supervised setting. For this talk, we first articulate when semi-supervised degeneracies are expected within our framework and then provide a general regularization strategy to address such circumstances. For statistical analysis we establish that the approach uses shrinking smoothers, provide circumstances for when the result is consistent, provide measures of inference and description, and establish clear connections to supervised models. Two applications are presented, the first involves classification of protein location on a cell using a network of protein interaction data, and the second involves classification of text documents with citation network information and text data.