Semi-Supervised Domain Adaptation via Joint Transductive and Inductive Subspace Learning

Abstract

Most existing shallow semi-supervised domain adaptation (SSDA) algorithms are based mainly on the framework adopting the maximum mean discrepancy (MMD) criterion, which is unstable and easily becomes stuck in a poor local minimum. Moreover, existing SSDA methods typically assume that the influence of the source domain is equivalent to that of the target domain, which is unreasonable and severely limits their performance. To address such drawbacks, we propose a novel SSDA framework derived from simple least squares regression (LSR) in a joint transductive and inductive learning paradigm, named transferable LSR (TLSR). Specifically, TLSR first learns domain-shared features using transfer component analysis (TCA) in a transductive paradigm. Then, TLSR augments the TCA features into the raw sample feature, formulating them into a block-diagonal matrix and training them in an inductive learning paradigm. This joint transductive and inductive learning paradigm helps alleviate the negative impacts of the MMD criterion of TCA but preserves the useful learned domain-shared knowledge. Moreover, the proposed block-diagonal input structure helps to separate the learned projections into independent domain-specific parts. Owing to the block-diagonal input structure, the influence of each domain can be reweighted, leading to significant improvements in performance. The experimental results demonstrate that the proposed TLSR outperforms the other shallow state-of-the-art competitors in 68 out of 90 cross-domain tasks.