College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, Jiangsu, China.
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang, China.
Neural Netw. 2022 Nov;155:498-511. doi: 10.1016/j.neunet.2022.08.031. Epub 2022 Sep 7.
Discriminative dictionary learning (DDL) aims to address pattern classification problems via learning dictionaries from training samples. Dictionary pair learning (DPL) based DDL has shown superiority as compared with most existing algorithms which only learn synthesis dictionaries or analysis dictionaries. However, in the original DPL algorithm, the discrimination capability is only promoted via the reconstruction error and the structures of the learned dictionaries, while the discrimination of coding coefficients is not considered in the process of dictionary learning. To address this issue, we propose a new DDL algorithm by introducing an additional discriminative term associated with coding coefficients. Specifically, a support vector machine (SVM) based term is employed to enhance the discrimination of coding coefficients. In this model, a structured dictionary pair and SVM classifiers are jointly learned, and an optimization method is developed to address the formulated optimization problem. A classification scheme based on both the reconstruction error and SVMs is also proposed. Simulation results on several widely used databases demonstrate that the proposed method can achieve competitive performance as compared with some state-of-the-art DDL algorithms.
判别字典学习（DDL）旨在通过从训练样本中学习字典来解决模式分类问题。基于字典对学习（DPL）的 DDL 已经显示出优于大多数现有算法的优越性，因为这些算法仅学习合成字典或分析字典。然而，在原始的 DPL 算法中，判别能力仅通过重建误差和学习字典的结构来促进，而在字典学习过程中没有考虑编码系数的判别。为了解决这个问题，我们提出了一种新的 DDL 算法，通过引入与编码系数相关的附加判别项。具体来说，我们使用基于支持向量机（SVM）的项来增强编码系数的判别能力。在这个模型中，联合学习结构化字典对和 SVM 分类器，并开发了一种优化方法来解决所提出的优化问题。还提出了一种基于重构误差和 SVM 的分类方案。在几个广泛使用的数据库上的仿真结果表明，与一些最先进的 DDL 算法相比，所提出的方法可以实现有竞争力的性能。
