文献类型：会议论文

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

通讯作者：Xu, BX[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing City Univ, Dept Technol & Ind Dev, Beijing, Peoples R 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

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

会议论文集：15th International Conference on Computational Intelligence and Security (CIS)

会议日期：DEC 13-16, 2019

会议地点：Macao, PEOPLES R CHINA

语种：英文

外文关键词：deep sparse representation; stacked autoencoder; pseudoinverse learning; dictionary Learning

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.