文章基本信息

标题：Graph-based Semi-Supervised Regression and Its Extensions
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作者：Xinlu Guo ; Kuniaki Uehara
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2015
卷号：6
期号：6
DOI：10.14569/IJACSA.2015.060636
出版社：Science and Information Society (SAI)
摘要：In this paper we present a graph-based semi-supervised method for solving regression problem. In our method, we first build an adjacent graph on all labeled and unlabeled data, and then incorporate the graph prior with the standard Gaussian process prior to infer the training model and prediction distribution for semi-supervised Gaussian process regression. Additionally, to further boost the learning performance, we employ a feedback algorithm to pick up the helpful prediction of unlabeled data for feeding back and re-training the model iteratively. Furthermore, we extend our semi-supervised method to a clustering regression framework to solve the computational problem of Gaussian process. Experimental results show that our work achieves encouraging results.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Semi-supervised learning; Graph-Laplacian; Re-gression; Gaussian Process; Feedback; Clustering