LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation

Lei Zhang, Wangmeng Zuo, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

206 Citations (Scopus)


We propose a novel reconstruction-based transfer learning method called latent sparse domain transfer (LSDT) for domain adaptation and visual categorization of heterogeneous data. For handling cross-domain distribution mismatch, we advocate reconstructing the target domain data with the combined source and target domain data points based on ℓ1-norm sparse coding. Furthermore, we propose a joint learning model for simultaneous optimization of the sparse coding and the optimal subspace representation. In addition, we generalize the proposed LSDT model into a kernel-based linear/nonlinear basis transformation learning framework for tackling nonlinear subspace shifts in reproduced kernel Hilbert space. The proposed methods have three advantages: 1) the latent space and the reconstruction are jointly learned for pursuit of an optimal subspace transfer; 2) with the theory of sparse subspace clustering, a few valuable source and target data points are formulated to reconstruct the target data with noise (outliers) from source domain removed during domain adaptation, such that the robustness is guaranteed; and 3) a nonlinear projection of some latent space with kernel is easily generalized for dealing with highly nonlinear domain shift (e.g., face poses). Extensive experiments on several benchmark vision data sets demonstrate that the proposed approaches outperform other state-of-the-art representation-based domain adaptation methods.
Original languageEnglish
Article number7378504
Pages (from-to)1177-1191
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number3
Publication statusPublished - 1 Mar 2016


  • domain adaptation
  • heterogeneous data
  • Transfer learning
  • visual categorization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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