关键词: 降维, 稀疏重构, 成对约束, 稀疏保持投影

Abstract: Concerning the deficiency of supervision information in the process of sparse reconstruction in Sparsity Preserving Projection (SPP), Pairwise Constraint-guided Sparsity Preserving Projection (PCSPP) was proposed, which introduced supervision information of must-link constraints and cannot-link constraints to guide sparse reconstruction in the process of sparsity reconstruction of training samples, making SPP fuse constraint supervise information efficiently. The experimental results in UMIST,YALE and AR face datasets show, in contrast to unsupervised sparsity preserving projections, our algorithm achieves approximately 5%~15% increase in recognition accuracy based on the nearest neighbor classifier and promotes efficiently the performance of dimensionality reduction classification.

Key words: Dimensionality reduction, Sparse reconstruction, Pairwise constraint, Sparsity preserving projections