A Generalized Meta-loss function for regression and classification using privileged information
This work addresses the problem of applying LUPI to regression and broader tasks for researchers and practitioners in machine learning, representing an incremental extension of the LUPI framework.
The authors tackled the limitation of existing learning using privileged information (LUPI) methods, which were mostly designed for classification, by proposing a generalized meta-loss function that enables LUPI for regression and other tasks, resulting in a model that outperforms the state-of-the-art in protein binding affinity prediction.
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.