Complexity reduction for null space-based linear discriminant analysis

In small sample size problems, the null space-based linear discriminant analysis (NLDA) provides a good discrimination performance but suffers from a complexity burden. Some schemes based on QR factorization and eigen-decomposition have been proposed for complexity reduction. In this paper, we propose a scheme based on Cholesky decomposition for a further reduction of the complexity.