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In this paper, a novel discriminative dictionary learning approach is proposed that attempts to preserve the local structure of the data while encouraging discriminability. The reconstruction error and sparsity inducing ℓ1-penalty of dictionary learning are minimized alongside a locality preserving and discriminative term. In this setting, each data point is represented by a sparse linear combination...
Active learning has been a popular area of research in recent years. It can be used to improve the performance of learning tasks by asking the labels of unlabeled data from the user. In these methods, the goal is to achieve the highest possible accuracy gain while posing minimum queries to the user. The existing approaches for active learning have been mostly applicable to the traditional binary or...
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