文献类型：期刊文献

英文题名：Deep Sparse Representation Classification with Stacked Autoencoder

作者：Xu, Bingxin[1]; Zhou, Xiuling[2]

第一作者：徐冰心

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China; [2] Department of Technology and Industry Development, Beijing City University, Beijing, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2019

起止页码：73-77

外文期刊名：Proceedings - 2019 15th International Conference on Computational Intelligence and Security, CIS 2019

收录：EI(收录号：20201308355511)

语种：英文

外文关键词：Deep learning - Learning systems

摘要：Sparse representation classification (SRC) is a new framework for classification and has been successfully applied to face recognition. However, in some cases it is not well to represent the test sample accurately, which tends to undermine the classification accuracy. In order to alleviate this issue, a deep sparse representation based classification (DSRC) method with a deep dictionary which learned by stacked autoencoder is proposed. Specifically, the proposed method trains a stacked autoencoders by pseudoinverse learning and used the hidden outputs to construct a deep dictionary. Given the deep dictionary, a hierarchical sparse representation based classification method is presented to determine the label for each test sample by a weighted residuals strategy. The experimental results show that the proposed method can achieve a comprehensively better performance compared with the state-of-the-art classification methods. ? 2019 IEEE.