Class-specific dictionary learning algorithm based on SVM sparse representation
In recent years,the dependence on large-scale training samples in deep learning has become a prominent issue.Dictionary learning algorithms have been proposed as a solution for small sample datasets.To further enhance the competitive advantage of dictionary learning in image classification,a class-specific dictionary learning algorithm based on support vector machine is proposed in this paper.The coefficient disparity constraint is introduced innovatively.The constraint term fuses the originally independent reconstruction,sparse,and discriminative terms into a unified learning framework,significantly improving the discriminative ability of the dictionary.It has been demonstrated through experiments that the classification performance of this model outperforms other state-of-the-art dictionary learning models.Additionally,a method to combine deep learning pre-training with dictionary learning algorithms is proposed,which has been experimentally demonstrated to significantly improve the classification performance of dictionary learning algorithms in large-scale training samples.
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