Robust Semi-Supervised Classification for Noisy Labels Based on Self-Paced Learning

Abstract

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.