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LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation

3 years 14 days ago
LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation
—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 -norm sparse coding. Furthermore, we propose a joint learning model for simultaneous optimization of the sparse coding and the optimal subspace representation. Additionally, 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 reconstruction are jointly learned for pursuit of an optimal subspace transfer; 2) with the theory of sparse subspace clustering (SSC), a few valuable source and target data points are formulated to reconstruct the target ...
Lei Zhang, Wangmeng Zuo, David Zhang
Added 11 Apr 2016
Updated 11 Apr 2016
Type Journal
Year 2016
Where TIP
Authors Lei Zhang, Wangmeng Zuo, David Zhang
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