Manifold-Regularized minimax probability machine

Authors:
Kazuki Yoshiyama;Akito Sakurai
Affiliations:
School of Science for Open and Environmental System, Keio University, Kohoku-ku, Yokohama, Japan;School of Science for Open and Environmental System, Keio University, Kohoku-ku, Yokohama, Japan
Venue:
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Year:
2011

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Abstract

In this paper we propose Manifold-Regularized Minimax Probability Machine, called MRMPM. We show that Minimax Probability Machine can properly be extended to semi-supervised version in the manifold regularization framework and that its kernelized version is obtained for non-linear case. Our experiments show that the proposed methods achieve results competitive to existing learning methods, such as Laplacian Support Vector Machine and Laplacian Regularized Least Square for publicly available datasets from UCI machine learning repository.