Surrogate Learning - An Approach for Semi-Supervised Classification

We consider the task of learning a classifier from the feature space $mathcal{X}$ to the set of classes $mathcal{Y} = {0, 1}$, when the features can be partitioned into class-conditionally independent feature sets $mathcal{X}_1$ and $mathcal{X}_2$. 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 $mathcal{X}_2$ to $mathcal{X}_1$ and 2) learning the class-conditional distribution of the feature set $mathcal{X}_1$. 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.