Semi-supervised Classification Using Local and Global Regularization

Fei Wang, Tao Li, Gang Wang, Changshui Zhang

In this paper, we propose a semi-supervised learning (SSL) algorithm based on local and global regularization. In the local regularization part, our algorithm constructs a regularized classifier for each data point using its neighborhood, while the global regularization part adopts a Laplacian regularizer to smooth the data labels predicted by those local classifiers. We show that some existing SSL algorithms can be derived from our framework. Finally we present some experimental results to show the effectiveness of our method.

Subjects: 12. Machine Learning and Discovery; 15. Problem Solving

Submitted: Apr 13, 2008

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