Robust multiple subspaces transfer for heterogeneous domain adaptation

Heterogeneous domain adaptation (HDA) aims to execute knowledge transfer from a source domain to a heterogeneous target domain. Previous works typically inject knowledge from the source and target domain into a common subspace. However, this may lead to the ineffectiveness of knowledge transfer due to the existence of heterogeneity. To overcome this drawback, in this paper, we propose a robust multiple subspaces transfer method for heterogeneous domain adaptation. Specifically, knowledge of two domains is projected into a union of multiple subspaces via a self-expressive model, in which joint distribution alignment and dynamic Laplacian regularization on self-repressive coefficients are included in the loss for characterizing transferability. Moreover, we provide a comprehensive analysis of stability, complexity, generalization, and convergence guarantee for the proposed method. Experiments on benchmark vision and Language datasets verify effectiveness of the proposed approach for heterogeneous domain adaptation. •We explore a multiple subspaces transfer method for heterogeneous domain adaptation.•We provide solid theoretical analysis (e.g. Convergence, Generalization) for the proposed methods.•The proposed method achieves the state-of-the-art prediction power.