Semi-Supervised Kernel Regression

Authors:
Meng Wang;Xian-Sheng Hua;Yan Song;Li-Rong Dai;Hong-Jiang Zhang
Affiliations:
University of Science and Technology of China, China;Microsoft Research Asia, China;University of Science and Technology of China, China;University of Science and Technology of China, China;Microsoft Research Asia, China
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

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Abstract

Insufficiency of training data is a major obstacle in machine learning and data mining applications. Many different semi-supervised learning algorithms have been proposed to tackle this difficulty by leveraging a large amount of unlabeled data. However, most of them focus on semi-supervised classification. In this paper we propose a semi-supervised regression algorithm named Semi-Supervised Kernel Regression (SSKR). While classical kernel regression is only based on labeled examples, our approach extends it to all observed examples using a weighting factor to modulate the effect of unlabeled examples. Experimental results prove that SSKR significantly outperforms traditional kernel regression and graph-based semi-supervised regression methods.