Fast feature selection by analyzing class regions approximated by ellipsoids

In the previous work by Abe et al. (1998), the backward feature selection method based on class regions approximated by ellipsoids was developed. In this paper, we accelerate feature selection by the forward selection search, the symmetric Cholesky factorization, and deletion of duplicated calculations between consecutive factorizations. The feature selection for two data sets shows that our method is faster than and as robust as the previous method