Semi-supervised learning using label mean

12 years 14 days ago
Semi-supervised learning using label mean
Semi-Supervised Support Vector Machines (S3VMs) typically directly estimate the label assignments for the unlabeled instances. This is often inefficient even with recent advances in the efficient training of the (supervised) SVM. In this paper, we show that S3VMs, with knowledge of the means of the class labels of the unlabeled data, is closely related to the supervised SVM with known labels on all the unlabeled data. This motivates us to first estimate the label means of the unlabeled data. Two versions of the meanS3VM, which work by maximizing the margin between the label means, are proposed. The first one is based on multiple kernel learning, while the second one is based on alternating optimization. Experiments show that both of the proposed algorithms achieve highly competitive and sometimes even the best performance as compared to the state-of-the-art semi-supervised learners. Moreover, they are more efficient than existing S3VMs.
Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou
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