Constraint Sparse Neighborhood Preserving Embedding

Abstract

Neighborhood Preserving Embedding (NPE) and extensions of NPE are hot research topics of data mining at present. An algorithm called Constraint Sparse Neighborhood Preserving Embedding (CSNPE) for dimensionality reduc- tion is proposed in the paper. The algorithm firstly creates the local sparse reconstructive relation information of samples; then, exacts the pairwise constrain information of samples. Finally, projections are obtained by infusing the two kinds of information with linear weighted way. In contrast to existing semi-supervised dimensionality reduction algorithms on NPE, CSNPE is available with the following characteristics: 1) Sparse reconstruction of local neighborhood of samples cost little because the number of them is limited. 2) CSNPE inherits the great robustness from sparse learning. 3) CSNPE infuses pairwise constrain information with weighted, preserving more discriminant information and local neighborhood sparse reconstruction information. Experiments conducted on real word facial databases demonstrate the effectiveness of the proposed algorithm.