文献类型：会议论文

英文题名：Broad and Pseudoinverse Learning for Autoencoder

作者：Xu, Bingxin[1];Guo, Ping[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 100101, Peoples R China;[2]Beijing Normal Univ, Sch Syst Sci, Image Pro & Patt Recog Lab, Beijing 100875, 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 100101, 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：IEEE International Conference on Systems, Man, and Cybernetics (SMC)

会议日期：OCT 07-10, 2018

会议地点：IEEE Syst Man & Cybernet Soc, Miyazaki, JAPAN

主办单位：IEEE Syst Man & Cybernet Soc

语种：英文

外文关键词：Autoencoder; Pseudoinverse learning algorithm; Receptive function; Broad learning

摘要：Autoencoder is one approach to automatically learn features from unlabeled data and received significant attention during the development of deep neural networks. However, the learning algorithm of autoencoder suffers from slow learning speed because of gradient descent based algorithms have many drawbacks. Pseudoinverse learning algorithm is a fast and fully automated method to train autoencoders. While when the dimension of data is far less than the number of data, the pseudoinverse learning can only obtain the optimal initial value of the autoencoder network and need further learning to achieve satisfactory results. In order to overcome the shortcomings mentioned above, we present a broad learning strategy to transform the input space to the high dimensional space through receptive function in this paper. The transformed data can be more suitable to pseudoinverse learning algorithm which can be obtained the accurate results of autoencoder efficiently. The experimental results show that the proposed method can achieve a comprehensively better performance in terms of training autoencoder efficiency and accuracy.