Co-Regularised Support Vector Regression

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

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: https://doi.org/10.6084/m9.figshare.5427241.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2017 Skopje, Macedonia, September 18–22, 2017 Proceedings, Part II
EditorsMichelangelo Ceci, Jaakko Hollmen, Ljupco Todorovski, Celine Vens, Saso Dzeroski
Place of PublicationCham Switzerland
PublisherSpringer
Chapter10535
Pages338-354
Number of pages17
ISBN (Electronic)9783319712468
ISBN (Print)9783319712451
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases : ECML PKDD 2017: ECML PKDD 2017 - Skopje, Macedonia
Duration: 18 Sep 201722 Sep 2017
Conference number: 15th
http://ecmlpkdd2017.ijs.si/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10535
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases : ECML PKDD 2017
Abbreviated titleECML PKDD 2017
CountryMacedonia
CitySkopje
Period18/09/1722/09/17
Internet address

Keywords

  • Co-regularisation
  • Kernel methods
  • Ligand affinity prediction
  • Multiple views
  • Rademacher complexity
  • Regression
  • Semi-supervised learning

Cite this

Ullrich, K., Kamp, M., Gärtner, T., Vogt, M., & Wrobel, S. (2017). Co-Regularised Support Vector Regression. In M. Ceci, J. Hollmen, L. Todorovski, C. Vens, & S. Dzeroski (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017 Skopje, Macedonia, September 18–22, 2017 Proceedings, Part II (pp. 338-354). (Lecture Notes in Computer Science; Vol. 10535 ). Cham Switzerland: Springer. https://doi.org/10.1007/978-3-319-71246-8_21