Discriminative Sparse Coding by Nuclear Norm-Driven Semi-Supervised Dictionary Learning.
PCM(2016)
摘要
In this paper, we propose a Nuclear norm-driven Semi-Supervised Dictionary Learning N-SSDL approach for classification. N-SSDL incorporates the idea of the recent label consistent KSVD with the label propagation process that propagates label information from labeled data to unlabeled data via balancing the neighborhood reconstruction error and the label fitness error. To provide a more reliable distance metric for measuring the neighborhood reconstruction error, we apply the nuclear-norm that is proved to be suitable for modeling the reconstruction error, where the reconstruction coefficients are computed based on the sparsely reconstructed training data rather than original ones. Besides, we also use the robust l2,1-norm regularization on the label fitness error so that the measurement is robust to noise and outliers. Extensive simulations on several datasets show that N-SSDL can deliver enhanced performance over other state-of-the-arts for classification.
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关键词
Semi-supervised learning, Discriminative sparse coding, Nuclear norm, l2,1-norm regularization
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