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Regularized Multi-Output Gaussian Convolution Process With Domain Adaptation
Journal article   Open access

Regularized Multi-Output Gaussian Convolution Process With Domain Adaptation

Xinming Wang, Chao Wang, Xuan Song, Levi Kirby and Jianguo Wu
IEEE transactions on pattern analysis and machine intelligence, Vol.45(5), pp.6142-6156
05/2023
DOI: 10.1109/TPAMI.2022.3205036
url
https://arxiv.org/pdf/2409.02778View
Open Access

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 constructed 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.

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