Semi-supervised Gaussian process regression and its feedback design

Xinlu Guo, Yoshiaki Yasumura, Kuniaki Uehara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Semi-supervised learning has received considerable attention in the machine learning literature due to its potential in reducing the need for expensive labeled data. The majority of the proposed algorithms, however, have been applied to the classification task. In this paper we present a graph-based semi-supervised algorithm for solving regression problem. Our method incorporates an adjacent graph, which is built on labeled and unlabeled data, with the standard Gaussian process (GP) prior to infer the new training and predicting distribution for semi-supervised GP regression (GPr). Additionally, in semi-supervised regression, the prediction of unlabeled data could contain some valuable information. For example, it can be seen as labeled data paired with the unlabeled data, and under some metrics, they can help to construct more accurate model. Therefore, we also describe a feedback algorithm, which can choose the useful prediction of unlabeled data for feedback to re-train the model iteratively. Experimental results show that our work achieves comparable performance to standard GPr.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 8th International Conference, ADMA 2012, Proceedings
Number of pages14
Publication statusPublished - 2012 Dec 1
Event8th International Conference on Advanced Data Mining and Applications, ADMA 2012 - Nanjing, China
Duration: 2012 Dec 152012 Dec 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7713 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference on Advanced Data Mining and Applications, ADMA 2012


  • Feedback
  • Gaussian process
  • Graph laplacian
  • Regression
  • Semi-supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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