Surrogate Learning - An Approach for Semi-Supervised Classification

We consider the task of learning a classifier from the feature space X to the set of classes Y = {0, 1}, when the features can be partitioned into class-conditionally independent feature sets X 1 and X 2. We show the surprising fact that the class-conditional independence can be used to represent the original learning task in terms of 1) learning a classifier from X 2 to X 1 and 2) learning the class-conditional distribution of the feature set X 1. This fact can be exploited for semi-supervised learning because the former task can be accomplished purely from unlabeled samples. We present experimental evaluation of the idea in two real world applications .