Semi-supervised Learning Based on Label Propagation through Submanifold

Authors:
Jiani Hu;Weihong Deng;Jun Guo
Affiliations:
Beijing University of Posts and Telecommunications, Beijing, China 100876;Beijing University of Posts and Telecommunications, Beijing, China 100876;Beijing University of Posts and Telecommunications, Beijing, China 100876
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

A semi-supervised learning algorithm is proposed based on label propagation through submanifold. The algorithm assumes that samples lying in a local neighborhood share the same labels and the global labels changing among submanifolds is sufficiently smooth. The algorithm firstly introduces a k-nearest neighbor graph to describe local neighborhood among the data set. And then, a cost function and a constraint equation are proposed, which stand for the global smoothness of the class labels' changing and the labeled samples' information respectively. The final semi-supervised learning task is converted to a typical quadratic program, whose optimal solution can minimize the cost function and satisfy the supervised constraint. Experimental results of the algorithm on toy data, digit recognition, and text classification demonstrate the feasibility and efficiency of the proposed algorithm.