.png){kind=icon}
返回
Transfer learning for regression via latent variable represented conditional distribution alignment
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2021.108110.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Since labelled data are expensive to generate both computationally and experimentally, how to establish data-driven models with limited data has become an important challenge in various engineering and scientific applications. Transfer learning has been used to improve the performance of many tasks by leveraging rich labelled data from related domains. This paper focuses on the transfer learning problem for regression under the situation of conditional distribution shift, which is a common scenario in manufacturing industry and has not been fully studied. Hence, we first propose a Latent variable represented Conditional Distribution Alignment method (LCDA), which exploits the low-dimensional latent representation to describe the global conditional distribution discrepancy. Then the properties of the latent variables are optimised by matching the kernel embedding conditional distribution between domains, so that the global distribution discrepancy can be aligned with the residual function generated by the latent variables. By combining domain priors with machine learning, the proposed method provides a potential direction to reduce labelling consumption for the intelligent manufacturing. Series of experiments on two industrial applications are conducted to validate the effectiveness and analyse the characteristics of the proposed method. (C) 2022 Elsevier B.V. All rights reserved.
Keyword:
Transfer learning
Conditional shift
Intelligent manufacturing
Regression
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing基于稀疏自编码器的深度迁移学习在制造业刀具剩余寿命预测中的应用
PREAI
分享收藏
分享收藏
查看更多内容