Surrogate learning

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 X1 and X2. We show that the class-conditional independence can be used to represent the original learning task in terms of 1) learning a classifier from X2 to X1 (in the sense of estimating the probability P (x1/x 2) ) and 2) learning the class-conditional distribution of the feature set X1. 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.