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In real-world applications, different kinds of learning and prediction errors are likely to incur different costs for the same system. Moreover, in practice, the cost label information is often available only for a few training samples. In a semi-supervised setting, label propagation is critical to infer the cost information for unlabeled training data. The existing methods typically conduct label propagation independently ahead of supervised cost-sensitive learning. The precomputed label information is kept fixed, which may become suboptimal in the subsequent learning process and hence degrade the overall system performance. In this paper, we develop a unified cost-sensitive framework for semi-supervised face recognition that can jointly optimize the inferred label information and the classifier in an iterative manner. Our experiments on face benchmark datasets demonstrate that in comparison with the state-of-the-art methods for label propagation and cost-sensitive learning, the proposed approach can significantly improve the overall system performance, especially in terms of classification errors associated with high costs.
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