Abstract: In a support vector data dcscription(SVDD)，the compact description of target data was given in a hyper spherical model which was determined by a small portion of data called support vectors. Despite the usefulness of the conventional SVDD,however,it may not identify the optimal solution of target description due to neglecting the structure of the given data. In order to mitigate this problem, a novel oncclass classifier named locality preserving data domain description(LPDI))was proposed which takes the data density into account by using of affine factor. Besides, the sequential minimal optimization was adopted to a山ust model parameters for applying in the large sample occasions. Experiments with various real data sets show promising results.

Key words: Affine factor,Support vector domain description,Secauential minimal optimization,One-class-classifier