Succinct Adaptive Manifold Transfer

Abstract

Capturing the relatedness of different domains is a key challenge in transferring knowledge across domains. In this paper, we propose an effective and efficient Gaussian process (GP) modelling framework, mTGPmk, that can explicitly model domain relatedness and adaptively control the space as well as the strength of knowledge transfer. mTGPmk takes both the discrepancy of input feature space and the discrepancy of predictive function into account in the transfer procedure. Specifically, mTGPmk adaptively selects a good latent manifold shared by different domains, and utilizes a parametric similarity coefficient to measure the predictive function covariance of different domains in this manifold. The latent shared manifold and the similarity coefficient are jointly learned in a coupled manner. By doing so, mTGPmk maximizes the strength of the shared knowledge transfer by choosing the transfer space with the best transfer capacity. More importantly, mTGPmk exploits a succinct and computationally efficient manifold learning approach so that it can be well trained with scarce target training data. Extensive experimental studies using 36 synthetic transfer tasks and 10 real-world transfer tasks show the effectiveness of mTGPmk on capturing the relatedness and the transfer adaptiveness.