Regularized Multi-Output Gaussian Convolution Process With Domain Adaptation

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.

Federated Condition Monitoring Signal Prediction With Improved Generalization

“…It pools all training and testing CM signals into a large multivariate GP, through which information transfer occurs from the historical to the testing units. Based on this idea, multiple studies further improve the model by incorporating auxiliary data [3], adaptively selecting more informative units [23], and distributing model inference efforts to individual units while preserving their privacy [24], among others. They all are capable of personalized predictions using non-parametric modeling, a key benefit inherited from GPs.…”

Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials

“…The objection function (Equation ( 2)) is implemented to realize the identification of different parameters. For the new input point x * , based on multivariate normal theory, the posterior distribution of the target output f t x * ð Þ can be expressed as follows [34]:…”

“…This method improve the accuracy of the model, but the computation burden also increases. Wang et al [34] established a MGCP to deal with the inconsistent input domain. The method marginalizes the inconsistent features to realize the domain adaptation.…”