Abstract In this paper, a new discriminative dictionary learning algorithm is introduced. An entropy based criterion is embedded into the objective function to enforce a proper structure for the dictionary items when decomposing signals of different classes. The proposed criterion influences the dictionary items to participate in the decomposition of a smaller number of classes as possible. Unlike the other methods, columns of the dictionary are not restricted to have pre-assigned labels and they are free to be representative of any class or to share features of several classes. The number of shared and discriminative items along with the number of dictionary items for each specific class is learned dynamically during the optimization process, depending on the complexity of the classification task and the distribution of different classes. The experimental results demonstrate that the proposed entropy based dictionary learning (EDL) algorithm outperforms other discriminative dictionary learning methods using several real-world image datasets.

中文翻译：

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用于图像分类的基于熵的字典学习

摘要 本文介绍了一种新的判别字典学习算法。将基于熵的标准嵌入到目标函数中，以便在分解不同类别的信号时为字典项强制执行适当的结构。所提出的标准影响字典项目参与尽可能少的类的分解。与其他方法不同，字典的列不限于具有预先分配的标签，它们可以自由代表任何类或共享多个类的特征。在优化过程中，根据分类任务的复杂性和不同类别的分布，动态学习共享和判别项目的数量以及每个特定类别的字典项目的数量。