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.