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In this paper, we propose an effective approach to semi-supervised classification through kernel-based sparse representation. The new method computes the sparse representation of data in the feature space, and then the learner is subject to a cost function which aims to preserve the sparse representing coefficients. By mapping the data into the feature space, the so-called “l 2 -norm problem”...
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