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

Authors:
Nicolas Loeff;David Forsyth;Deepak Ramachandran
Affiliations:
University of Illinois, Urbana, IL;University of Illinois, Urbana, IL;University of Illinois, Urbana, IL
Venue:
Proceedings of the 25th international conference on Machine learning
Year:
2008

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Abstract

We describe a manifold learning framewor 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.