Abstract
Due to the inherent nature of kernel implementation, the kernel Fisher discriminant suffers from the small sample size problem. In this paper, we introduce a novel variant of the kernel Fisher discriminant formulation to circumvent this problem. By adopting a two-fold regularization scheme on the scatter matrices, we show both effectiveness and reliability of the proposed method particularly regarding the small sample size and the lack of dimensionality issues.
