A supervised neighborhood preserving embedding for face recognition

Neighborhood preserving embedding (NPE) is an approximation to locally linear embedding (LLE), which has an ability to preserve local neighborhood structure on data manifold. As an unsupervised dimensionality reduction method, NPE can be applied to face recognition for preprocessing. However, NPE could not utilize the label information in the classification tasks. To make the data in a reduced subspace separable, this paper proposes a supervised neighborhood preserving embedding which could learn a projection matrix by using both the geometrical manifold structure and the label information of the given data. In addition, the projection matrix could be found by solving a linear set of equations. Experimental results on ORL and Yale face image datasets show that the proposed method has a high recognition rate.