关键词: 降维, 近邻稀疏保留投影, 成对约束, 人脸识别

Abstract: As sparsity preserving projection algorithm is a globally unsupervised dimensionality reduction method, which turns out to be invalid to solve the lack of training samples or large differences in the same class, a locally sparsity persevering projection algorithm considering pairwise constraint is proposed. Sparse coefficients are obtained using neighbor samples in order to preserve local structure information. Then pairwise constraints supervision idea is taken for reference, applying sample labels to guide sparse reconstruction. Objective function is defined to catch the low-dimensional subspace which can best preserve the discriminant information contained in sparse coefficients. The effectiveness and feasibility of this proposed algorithm is verified by face recognition experiments from the perspective of recognition rate and runtime.

Key words: dimensionality reduction, locally sparsity preserving projection, pairwise constraint, face recognition