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论文类型：期刊论文

发表时间：2011-04-01

发表刊物：Journal of Information and Computational Science

收录刊物：EI、Scopus

卷号：8

期号：4

页面范围：601-608

ISSN号：15487741

摘要：There has been significant recent interest in extending supervised algorithms to semi-supervised form which preserve local structures of the unlabeled samples. However, how to choose the homogeneous points is still an open-problem. In this paper, by introducing the sparse Lorentzian metric, we propose a general framework to extend supervised algorithms to semi-supervised case. Our proposed techniques can find the homogeneous points of the unlabeled samples in a more natural way. The learnt sparse Lorentzian metric tensors can also keep both the local structure of the unlabeled samples and their global geometrical structure. The experimental results on face recognition show that our algorithm achieves better recognition accuracy. Copyright ? 2011 Binary Information Press.