Transfer Kernel Learning for Multi-Source Transfer Gaussian Process Regression

Multi-source transfer regression is a practical and challenging problem where capturing the diverse relatedness of different domains is the key of adaptive knowledge transfer. In this article, we propose an effective way of explicitly modeling the domain relatedness of each domain pair through transfer kernel learning. Specifically, we first discuss the advantages and disadvantages of existing transfer kernels in handling the multi-source transfer regression problem. To cope with the limitations of the existing transfer kernels, we further propose a novel multi-source transfer kernel k(ms). The proposed (ms) assigns a learnable parametric coefficient to model the relatedness of each inter-domain pair, and simultaneously regulates the relatedness of the intra-domain pair to be 1. Moreover, to capture the heterogeneous data characteristics of multiple domains, k(ms) exploits different standard kernels for different domain pairs. We further provide a theorem that not only guarantees the positive semi-definiteness of k(ms) but also conveys a semantic interpretation to the learned domain relatedness. Moreover, the theorem can be easily used in the learning of the corresponding transfer Gaussian process model with k(ms) . Extensive empirical studies show the effectiveness of our proposed method on domain relatedness modelling and transfer performance.

Automated summary

In this article, an effective method of explicitly modeling the domain relatedness of each domain pair through transfer kernel learning is proposed. To overcome the limitations of existing transfer kernels, a novel multi-source transfer kernel k(ms) is further introduced. The proposed method assigns a learnable parametric coefficient to model the relatedness of each inter-domain pair and simultaneously regulates the relatedness of the intra-domain pair to be 1. Experimental results demonstrate the effectiveness of the proposed method in domain relatedness modeling and transfer performance.