摘要
提出一种基于深度学习的不完整大数据填充算法.算法首先以自动编码机为基础建立填充自动编码机.在此基础上,构建深度填充网络模型,分析不完整大数据的深度特征并根据逐层训练思想和反向传播算法计算网络参数.最后利用深度填充网络来还原不完整大数据,对缺失值进行填充.实验表明,提出的算法能够有效提高不完整大数据的填充精度.
This paper presents an impuation algorithm based on learning for incomplete big data.The proposed algorithm establishs a novel auto-encoder,called imputation auto-encoder,and then builds a deep imputation network model to analyze the deep features of incomplete big data and to calculate network parameters based on drill training ideas and back-propagation algorithm.Finally,the deep imputation network is used to impute the missing values.Experimental results show that the proposed algorithm can effectively improve the imputation accuracy for incomplete big data.
出处
《微电子学与计算机》
CSCD
北大核心
2014年第12期173-176,共4页
Microelectronics & Computer
基金
国家重点自然科学基金(U1301253)
辽宁省自然科学基金(201202032)
