内蒙古民族大学学报（自然科学版）2016，Vol.31Issue(1)：21-25,5.DOI:10.14045/j.cnki.15-1220.2016.01.006
一种稀疏降噪自编码神经网络研究
Study on Sparse De-noising Auto-Encoder Neural Network
摘要
Abstract
In recent years, the study about auto-encoder neural network based on deep learning has been a hot topic in research of data dimension reduction, which can eliminate irrelevant and redundant information effectively and im-prove the efficiency of the inherent characteristics of the learning data. More robust expression for input data can be trained through adding noise at the raw data preprocessing, which thereby enhances the generalization of auto-encod-er neural network model for input data. Sparse De-noising Auto-Encoder(SDAE)was proposed. De-noising au-to-encoder neural networks were enhanced based on the idea of sparsity which enables abstract features of sparse representation to become more effective for data classification. Experimental results show that classification accuracy of SDAE is better than that of traditional auto-encoder neural network and de-noising auto-encoder neural network.关键词
数据降维/降噪/稀疏/稀疏降噪自编码神经网络Key words
Dimension reduction/De-noise/Sparse/Sparse De-noising Auto-Encoder neural network分类
信息技术与安全科学引用本文复制引用
张成刚,姜静清..一种稀疏降噪自编码神经网络研究[J].内蒙古民族大学学报（自然科学版）,2016,31(1):21-25,5.基金项目
国家自然科学基金资助项目(61163034,61373067) （61163034,61373067）
内蒙古自治区自然科学基金资助项目(2013MS0910,2013MS0911) （2013MS0910,2013MS0911）
