Projected Estimators for Robust Semi-supervised Classification
This provides a safe, assumption-free method for semi-supervised classification, though it is incremental as it builds on existing quadratic loss frameworks.
The authors tackled the problem of ensuring semi-supervised classification methods outperform supervised ones by using a projected estimator with quadratic loss, guaranteeing no worse performance on training data. They demonstrated this theoretically and validated it on benchmark datasets, showing conservative improvements.
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Using a projection of the supervised estimate onto a set of constraints imposed by the unlabeled data, we find we can safely improve over the supervised solution in terms of this quadratic loss. Unlike other approaches to semi-supervised learning, the procedure does not rely on assumptions that are not intrinsic to the classifier at hand. It is theoretically demonstrated that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over the supervised solution. The characteristics of our approach are explicated using benchmark datasets to further understand the similarities and differences between the quadratic loss criterion used in the theoretical results and the classification accuracy often considered in practice.