  Learning using privileged information (LUPI) is a powerful heterogenous
feature space machine learning framework that allows a machine learning model
to learn from highly informative or privileged features which are available
during training only to generate test predictions using input space features
which are available both during training and testing. LUPI can significantly
improve prediction performance in a variety of machine learning problems.
However, existing large margin and neural network implementations of learning
using privileged information are mostly designed for classification tasks. In
this work, we have proposed a simple yet effective formulation that allows us
to perform regression using privileged information through a custom loss
function. Apart from regression, our formulation allows general application of
LUPI to classification and other related problems as well. We have verified the
correctness, applicability and effectiveness of our method on regression and
classification problems over different synthetic and real-world problems. To
test the usefulness of the proposed model in real-world problems, we have
evaluated our method on the problem of protein binding affinity prediction. The
proposed LUPI regression-based model has shown to outperform the current
state-of-the-art predictor.

Machine Learning;Machine Learning