Regularized Multi-output Gaussian Convolution Process with Domain Adaptation

TL;DR

This paper introduces a regularized multi-output Gaussian process framework with domain adaptation, effectively addressing negative transfer and input domain inconsistency in transfer learning scenarios.

Contribution

It proposes a novel sparse covariance matrix using convolution process and a domain adaptation method to improve transfer learning with multi-output Gaussian processes.

Findings

Outperforms state-of-the-art benchmarks in simulations

Effectively handles negative transfer issues

Successfully manages domain inconsistency in real case study

Abstract

Multi-output Gaussian process (MGP) has been attracting increasing attention as a transfer learning method to model multiple outputs. Despite its high flexibility and generality, MGP still faces two critical challenges when applied to transfer learning. The first one is negative transfer, which occurs when there exists no shared information among the outputs. The second challenge is the input domain inconsistency, which is commonly studied in transfer learning yet not explored in MGP. In this paper, we propose a regularized MGP modeling framework with domain adaptation to overcome these challenges. More specifically, a sparse covariance matrix of MGP is proposed by using convolution process, where penalization terms are added to adaptively select the most informative outputs for knowledge transfer. To deal with the domain inconsistency, a domain adaptation method is proposed by marginalizing inconsistent features and expanding missing features to align the input domains among different outputs. Statistical properties of the proposed method are provided to guarantee the performance practically and asymptotically. The proposed framework outperforms state-of-the-art benchmarks in comprehensive simulation studies and one real case study of a ceramic manufacturing process. The results demonstrate the effectiveness of our method in dealing with both the negative transfer and the domain inconsistency.