Semi-supervised Gaussian process regression and its feedback design

Xinlu Guo, Yoshiaki Yasumura, Kuniaki Uehara

研究成果: Conference contribution

1 被引用数 (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.

ホスト出版物のタイトルAdvanced Data Mining and Applications - 8th International Conference, ADMA 2012, Proceedings
出版ステータスPublished - 2012 12 1
イベント8th International Conference on Advanced Data Mining and Applications, ADMA 2012 - Nanjing, China
継続期間: 2012 12 152012 12 18


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7713 LNAI


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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