ManifoldBoost: Stagewise function approximation for fully-, semi-and un-supervised learning

Nicolas Loeff, David Forsyth, Deepak Ramachandran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe a manifold learning framework that naturally accommodates supervised learning, partially supervised learning and unsupervised clustering as particular cases. Our method chooses a function by minimizing loss subject to a manifold regularization penalty. This augmented cost is minimized using a greedy, stagewise, functional minimization procedure, as in Gradientboost. Each stage of boosting is fast and efficient. We demonstrate our approach using both radial basis function approximations and trees. The performance of our method is at the state of the art on many standard semi-supervised learning benchmarks, and we produce results for large scale datasets.

Original languageEnglish (US)
Title of host publicationProceedings of the 25th International Conference on Machine Learning
Pages600-607
Number of pages8
StatePublished - Nov 26 2008
Event25th International Conference on Machine Learning - Helsinki, Finland
Duration: Jul 5 2008Jul 9 2008

Publication series

NameProceedings of the 25th International Conference on Machine Learning

Other

Other25th International Conference on Machine Learning
CountryFinland
CityHelsinki
Period7/5/087/9/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

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  • Cite this

    Loeff, N., Forsyth, D., & Ramachandran, D. (2008). ManifoldBoost: Stagewise function approximation for fully-, semi-and un-supervised learning. In Proceedings of the 25th International Conference on Machine Learning (pp. 600-607). (Proceedings of the 25th International Conference on Machine Learning).