Abstract
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 language | English |
|---|---|
| Pages (from-to) | 192-200 |
| Number of pages | 9 |
| Journal | Pattern Recognition Letters |
| Volume | 37 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2014 Feb 1 |
Keywords
- 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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Cite this
Yoshiyama, K., & Sakurai, A. (2014). Laplacian minimax probability machine. Pattern Recognition Letters, 37(1), 192-200. https://doi.org/10.1016/j.patrec.2013.01.004