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Recent years have witnessed a growing interest in the sparse representation problem. Prior work demonstrated that adaptive dictionary learning techniques can greatly improve the performance of sparse representation approaches. Existing techniques mainly focus on the reconstructive accuracies and the discriminative power of the learned dictionary, whereas the mutual incoherence between any two basis...
In this paper, we propose a novel sparse representation method for classification called similarity weighted sparse representation (SWSR). The similarity weighted ℓ1-norm minimization, where the weighted matrix is constructed by incorporating the similarity information between the test sample and the entire training samples, is presented as an alternative to ℓ0-norm minimization to seek the optimal...
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...
Due to the fact that many objects in the real world can be naturally represented as tensors, tensor subspace analysis has become a hot research area in pattern recognition and computer vision. However, existing tensor subspace analysis methods cannot provide an intuitionistic nor semantic interpretation for the projection matrices. In this paper, we propose Sparse Tensor Principal Component Analysis...
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