Transfer learning for regression via latent variable represented conditional distribution alignment

摘要

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.