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 |
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Keywords
- Laplacian RLS
- Laplacian SVM
- Manifold regularization
- Minimax probability machine
- Semi-supervised learning
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Laplacian minimax probability machine. / Yoshiyama, K.; Sakurai, A.
In: Pattern Recognition Letters, Vol. 37, No. 1, 01.02.2014, p. 192-200.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Laplacian minimax probability machine
AU - Yoshiyama, K.
AU - Sakurai, A.
PY - 2014/2/1
Y1 - 2014/2/1
N2 - 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.
AB - 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.
KW - Laplacian RLS
KW - Laplacian SVM
KW - Manifold regularization
KW - Minimax probability machine
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=84891628135&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84891628135&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2013.01.004
DO - 10.1016/j.patrec.2013.01.004
M3 - Article
VL - 37
SP - 192
EP - 200
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
SN - 0167-8655
IS - 1
ER -