计算机工程与应用2017，Vol.53Issue(5)：134-139,6.DOI:10.3778/j.issn.1002-8331.1507-0148
基于栈式去噪自动编码器的边际Fisher分析算法
Marginal Fisher analysis algorithm based on stacked denoising autoencoders
颜丹 1蒋加伏1
作者信息
- 1. 长沙理工大学 计算机与通信工程学院,长沙 410114
- 折叠
摘要
Abstract
Feature learning is a key issue in the field of pattern recognition. By unsupervised pre-trainning and supervised fine-tuning, the deep neural network based on autoencoders can effectively extract critical information of data to form features. A marginal Fisher analysis algorithm based on stacked denoising autoencoders has been proposed which can further improve the ability of representation learning by applying marginal Fisher analysis to the supervised fine-tuning phase. Experimental results show that the algorithm achieves better recognition results compared to the standard stacked denoising autoencoders and the deep belief networks based on restricted Boltzmann machine.关键词
特征学习/深度学习/人工神经网络/栈式去噪自动编码器/边际Fisher分析Key words
feature learning/deep learning/artificial neural network/stacked denoising autoencoders/marginal Fisher analysis分类
信息技术与安全科学引用本文复制引用
颜丹,蒋加伏..基于栈式去噪自动编码器的边际Fisher分析算法[J].计算机工程与应用,2017,53(5):134-139,6.
