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• W1554522139 abstract "We propose a novel joint dynamic sparsity regularization for joint learning of multiple tasks (i.e., multiple observations of the same physical event by a set of homogeneous or heterogenous sensors). The proposed method not only combines the strength of different tasks but also has the flexibility of selecting a set of different atoms for each task, with a class-wise constraint, which is more flexible and even crucial in many real-world scenarios. We develop an efficient learning algorithm for the joint dynamic sparsity using the accelerated proximal gradient descent. The proposed method is applied to a multi-view face recognition task and the experimental results on the public CMU Multi-PIE dataset verify its effectiveness." @default.
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• W1554522139 date "2012-12-01" @default.
• W1554522139 modified "2023-10-12" @default.
• W1554522139 title "Joint dynamic sparse learning and its application to multi-view face recognition" @default.
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