  Many researches have been devoted to learn a Mahalanobis distance metric,
which can effectively improve the performance of kNN classification. Most
approaches are iterative and computational expensive and linear rigidity still
critically limits metric learning algorithm to perform better. We proposed a
computational economical framework to learn multiple metrics in closed-form.
