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.