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

Multiple Closed-Form Local Metric Learning for K-Nearest Neighbor Classifier