Abstract: The support vector domain description (SVDD) is a robust data domain description method. Its performance, however, is strongly influenced by kernel parameter. In this paper, a novel parameter-optimizing algorithm is presented based on the idea that the optimal parameter can lead to the distribution of the mapped data in the feature space to be a hyper-sphere shape. Firstly, based on an orthogonal basis of the subspace spanned by the mapped data, a way is given to capture the structure of the entire mapped data, which avoids the problem that the mapped data cannot be expressed in an explicit form. Secondly, based on the maximum-entropy non-Gaussian measurement, a new criterion is presented for estimating the degree for a distribution to be closes to the hyper-sphere area and it is used to select the suitable kernel parameter. The experiments on simulated data and real-world data demonstrate the effectiveness of the proposed method.