In the sparse representation of a target sample, most nonzero coefficients belong to the neighbors of the target sample. Combining this observation with the theory of manifold learning, we propose a novel unsupervised feature extraction approach named local sparsity preserving projection (LSPP). LSPP sparsely reconstructs a target training sample from merely its neighbors, and seeks a subspace where the local sparse reconstructive relations among all training samples are preserved. To improve the discriminating power of LSPP, we further propose a supervised LSPP (SLSPP), which incorporates the class information of neighbor samples into local sparse representation. Experimental results on the AR and CAS-PEAL face databases demonstrate the effectiveness of LSPP and SLSPP, as compared with related feature extraction methods.
['Xiao-Yuan Jing', 'Sheng Li', 'Songhao Zhu', 'Qian Liu', 'Jingyu Yang', 'Jiasen Lu']
Supervised local sparsity preserving projection for face feature extraction