Abstract:

As many natural images are symmetrical and most of data distributions exhibit a manifold structure,a symmetric locally-preserving semi-supervised dimensionality reduction ( SLPSDR) algorithm is proposed.In the algorithm,a matrix is used to define the relationship between dimensionality reduction mapping matrix elements,so as to minimize the difference between the matrix elements of symmetric pixel points in an image.In order to keep the manifold structure of data by using the training samples without a label,it is required that the neighborhood relationship of each point in a low-dimension space is similar to that in a high-dimension space.The experimental results on CMU PIE,Extend YaleB,ORL and AR face databases show that the symmetric feature of image data causes the SLPSDRalgorithm to be superior to other contrastive dimensionality reduction algorithms.

Key words: symmetry constraint, semi-supervised learning, dimensionality reduction, face recognition