Semi-supervised Learning with Multimodal Perturbation

Authors:
Lei Su;Hongzhi Liao;Zhengtao Yu;Jiahua Tang
Affiliations:
School of Software, Yunnan University, Kunming, China 650091;School of Software, Yunnan University, Kunming, China 650091;School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China 650051;School of Software, Yunnan University, Kunming, China 650091
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

In this paper, a new co-training style semi-supervised algorithm is proposed, which employs Bagging based multimodal perturbation to label the unlabeled data. In detail, through perturbing the training data, input attributes and learning parameters together, the algorithm generates accurate but diversity k-nearest neighbor classifiers. These classifiers are refined using unlabeled examples which are labeled if the other classifiers agree on the labeling. Experimental results show that the semi-supervised algorithm could effectively improve the classification generalization by utilizing the unlabeled data.