Presentation 2012-11-08
Local Semi-supervised Gaussian Process Regression based-on Clustering
Xinlu GUO, Yoshiaki YASUMURA, Kuniaki UEHARA,
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Abstract(in English) The majority of the existing graph-based semi-supervised learning algorithms have been applied to the classification task. In this paper we propose a graph-based semi-supervised Gaussian process (GP) algorithm for solving regression problem. Our method incorporates an adjacent graph, which is built on labeled and unlabeled data, with the standard GP prior to infer the new training and predicting distribution for semi-supervised GP regression (GPr). Furthermore, we extend our semi-supervised method to a clustering framework for reducing the computational cost in GP. Experimental results show that our work achieves encouraging results.
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Keyword(in English) Semi-supervised Learning / Regression / Gaussian process / Graph Laplacian / Clustering
Paper # IBISML2012-86
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Committee IBISML
Conference Date 2012/10/31(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Local Semi-supervised Gaussian Process Regression based-on Clustering
Sub Title (in English)
Keyword(1) Semi-supervised Learning
Keyword(2) Regression
Keyword(3) Gaussian process
Keyword(4) Graph Laplacian
Keyword(5) Clustering
1st Author's Name Xinlu GUO
1st Author's Affiliation Graduate School of System Informatics, Kobe University()
2nd Author's Name Yoshiaki YASUMURA
2nd Author's Affiliation College of Engineering, Shibaura Institute of Technology
3rd Author's Name Kuniaki UEHARA
3rd Author's Affiliation Graduate School of System Informatics, Kobe University
Date 2012-11-08
Paper # IBISML2012-86
Volume (vol) vol.112
Number (no) 279
Page pp.pp.-
#Pages 8
Date of Issue