Laplacian minimax probability machine

K. Yoshiyama, A. Sakurai

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


In this paper, we propose a Laplacian minimax probability machine, which is a semi-supervised version of minimax probability machine based on the manifold regularization framework. We also show that the proposed method can be kernelized on the basis of a theorem similar to the representer theorem for non-linear cases. Experiments confirm that the proposed methods achieve competitive results, as compared to existing graph-based learning methods such as the Laplacian support vector machine and the Laplacian regularized least square, for publicly available datasets from the UCI machine learning repository.

Original languageEnglish
Pages (from-to)192-200
Number of pages9
JournalPattern Recognition Letters
Issue number1
Publication statusPublished - 2014 Feb 1


  • Laplacian RLS
  • Laplacian SVM
  • Manifold regularization
  • Minimax probability machine
  • Semi-supervised learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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