A new algorithm for computing the minimal enclosing sphere in feature space

The problem of computing the minimal enclosing sphere (MES) of a set of points in the high dimensional kernel-induced feature space is considered. In this paper we develop an entropy-based algorithm that is suitable for any Mercer kernel mapping. The proposed algorithm is based on maximum entropy principle and it is very simple to implement. The convergence of the novel algorithm is analyzed and the validity of this algorithm is confirmed by preliminary numerical results.