sparsecoding was shown to be able to find succinct representations of stimuli. Recently, it has been successfully applied to a variety of problems in image processing analysis. sparsecoding models data vectors as a ...
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ISBN:
(纸本)9781479957521
sparsecoding was shown to be able to find succinct representations of stimuli. Recently, it has been successfully applied to a variety of problems in image processing analysis. sparsecoding models data vectors as a linear combination of a few elements from a dictionary. However, most existing sparsecoding methods are applied for a single task on a single dataset. The learned dictionary is then possibly biased towards the specific dataset and lacks of generalization abilities. In light of this, in this paper we propose a multitasksparsecoding approach by uncovering a shared subspace among heterogeneous datasets. The proposed multi-taskcoding strategy leverages the commonality benefit from different datasets. Moreover, our multi-taskcoding framework is capable of direct classification by incorporating label information. Experimental results show that the dictionary learned by our approach has more generalization abilities and our model performs better classification compared to the model learned from only one dataset or the model learned from simply pooling different datasets together.
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