Solving the small sample size problem of LDA

The small sample size problem is often encountered in pattern recognition. It results in the singularity of the within-class scattering matrix S/sub w/ in linear discriminant analysis (LDA). Different methods have been proposed to solve this problem in face recognition literature. Some methods reduce the dimension of the original sample space and hence unavoidably remove the null space of S/sub w/, which has been demonstrated to contain considerable discriminative information; whereas other methods suffer from the computational problem. In this paper, we propose a new method making use of the null space of S/sub w/ effectively and solve the small sample size problem of LDA. We compare our method with several well-known methods, and demonstrate the efficiency of our method.