计算机科学与探索2019，Vol.13Issue(2)：322-329,8.
用于域适应的多边缘降噪自动编码器
Multi-Marginalized Denoising Autoencoders for Domain Adaptation*
杨帅 1胡学钢 1张玉红1
作者信息
- 1. 合肥工业大学 计算机与信息学院,合肥 230009
- 折叠
摘要
Abstract
Neural network models are used to address domain adaptation. As a model of neural network, marginalized stacked denoising autoencoders (mSDA) can extract and encode more robust feature space. mSDA tends to learn a common and robust feature representation to solve the problem of domain adaptation by marginalizing corruption with noise to the source and target domain data. However, mSDA uses the same marginalized and denoising method to corrupt all features. But in fact, features have different effects on the classification. This paper tries to corrupt the different features with a variant noise, and proposes the approach named multi-marginalized denoising autoencoders (M-MDA) for domain adaptation. Firstly, a polarity index WLLRU (weighted log-likelihood ratio update) which is improved from weight likelihood ratio, is proposed to distinguish the shared features from specific features. Then, the shared features and specific features are corrupted with different noises, and the noise is computed according to the distance of features between the source and target domain. And then marginalized denoising autoencoders (MDA) is used to learn a more robust feature space with the corrupted data. Lastly, the new feature space is corrupted again to enhance the proportion of shared features. The experimental results show that the proposed method outperforms state-of-the-art methods in cross-domain sentiment classification.关键词
情感分类/跨领域/噪音/边缘堆叠降噪自动编码器(mSDA)Key words
sentiment classification/ cross-domain/ noise/ marginalized stacked denoising autoencoders (mSDA)分类
信息技术与安全科学引用本文复制引用
杨帅,胡学钢,张玉红..用于域适应的多边缘降噪自动编码器[J].计算机科学与探索,2019,13(2):322-329,8.
