燕山大学学报2024，Vol.48Issue(5)：437-445,9.DOI:10.3969/j.issn.1007-791X.2024.05.006
基于SVM稀疏表示的类特别字典学习算法
Class-specific dictionary learning algorithm based on SVM sparse representation
摘要
Abstract
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.关键词
字典学习/稀疏表示/支持向量机/系数相异性约束项Key words
dictionary learning/sparse representation/support vector machine/coefficient disparity constraint分类
信息技术与安全科学引用本文复制引用
宋银涛,杨宝庆,刘计,赵宇,闫敬..基于SVM稀疏表示的类特别字典学习算法[J].燕山大学学报,2024,48(5):437-445,9.基金项目
国家自然科学基金资助项目(62205283) （62205283）
