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Co-regularised support vector regression

: Ullrich, Katrin; Kamp, Michael; Gärtner, Thomas; Vogt, Martin; Wrobel, Stefan


Ceci, M.:
Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2017 : Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10535)
ISBN: 978-3-319-71245-1 (Print)
ISBN: 978-3-319-71246-8 (Online)
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2017, Skopje>
Conference Paper
Fraunhofer IAIS ()

We consider a semi-supervised learning scenario for regression, where only few labelled examples, many unlabelled instances and different data representations (multiple views) are available. For this setting, we extend support vector regression with a co-regularisation term and obtain co-regularised support vector regression (CoSVR). In addition to labelled data, co-regularisation includes information from unlabelled examples by ensuring that models trained on different views make similar predictions. Ligand affinity prediction is an important real-world problem that fits into this scenario. The characterisation of the strength of protein-ligand bonds is a crucial step in the process of drug discovery and design. We introduce variants of the base CoSVR algorithm and discuss their theoretical and computational properties. For the CoSVR function class we provide a theoretical bound on the Rademacher complexity. Finally, we demonstrate the usefulness of CoSVR for the affinity prediction task and evaluate its performance empirically on different protein-ligand datasets. We show that CoSVR outperforms co-regularised least squares regression as well as existing state-of-the-art approaches for affinity prediction. Code and data related to this chapter are available at: