Sparse representation has received an increasing amount of interest in pattern classification due to its robustness. In this paper, a domain adaptation learning(DAL) approach is explored based on a sparsity preserving model, which assumes that each data point can be sparsely reconstructed. The proposed robust DAL algorithm, called sparse label propagation domain adaptation learning(SLPDAL), propagates the labels from labeled points in the source domain to the unlabeled dataset in the target domain using tho...
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