Joint Sparse Locality Preserving Projections

Manifold learning and feature selection have been widely studied in face recognition in the past two decades. This paper focuses on making use of the manifold structure of datasets for feature extraction and selection. We propose a novel method called Joint Sparse Locality Preserving Projections (JSLPP). In order to preserve the manifold structure of datasets, we first propose a manifold-based regression model by using a nearest-neighbor graph, then the L-2,L-1-norm regularization term is imposed on the model to perform feature selection. At last, an efficient iterative algorithm is designed to solve the sparse regression model. The convergence analysis and computational complexity analysis of the algorithm are presented. Experimental results on two face datasets indicate that JSLPP outperforms six classical and state-of-the-art dimensionality reduction algorithms.