Most one-class classification algorithms measure similarity based on Euclidean distance between samples.Unfortunately

the Euclidean distance couldn't reveal the internal distribution of some datasets

and so reduced the descriptive ability of these methods.A distance metric learning algorithm was proposed to improve the performance of one-class classifiers in this paper.Compared with existing distance metric learning algorithm

the algorithm only needed to provide target class data

it could effectively solve distance metric learning problem for one-class samples in high-dimensional space by imposing sample distribution prior and sparsity prior with l1-norm constraint on the distance metric

and the formulation could be efficiently optimized in a block coordination descent algorithm.The learned metric can be easily embedded into one-class classifiers

the simulation experimental results show that the learned metric can effectively improve the description performance of one-class classifiers

in particular the description of covering classification model and obtain better generalization ability of one-class classifiers.