Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 23, Issue 12, Pages 1806-1810Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2016.2619352
Keywords
Locally linear reconstruction (LLR); manifold regularization (MR); self-paced learning (SPL); semi-supervised classification (SSC); semi-supervised learning
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Funding
- National Natural Science Foundation of China [61503263, 61373114, 61473212, 61203241]
- Scientific Research Level Improvement Quota Project of Capital University of Economics and Business
- Natural Science Foundation of Zhejiang Province [LY15F030011]
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Data labeling is a tedious and subjective task that can be time consuming and error-prone; however, most learning algorithms are sensitive to noisy labels. This problem raises the need to develop algorithms that can exploit large amount of unlabeled data and also be robust to noisy label information. In this letter, we propose a novel semi-supervised classification framework that is robust to noisy labels, named self-paced manifold regularization. The proposed framework naturally integrates self-paced learning regime into the manifold regularization framework for selecting labeled training samples in a theoretically sound manner, and utilizes locally linear reconstructions to control the smoothness of the classifier with respect to the manifold structure of data. Finally, the alternative search strategy is adopted for the proposed framework to obtain the classifier. The proposed method can not only suppress the negative effect of noisy initial labels in semi-supervised learning, but also obtain an explicit multiclass classifier for newly coming data points. Experimental results demonstrate the effectiveness of the proposed method.
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