Multiple Closed-Form Local Metric Learning for K-Nearest Neighbor Classifier
This addresses efficiency and flexibility issues in metric learning for researchers and practitioners using kNN classifiers, though it appears incremental.
The paper tackled the computational expense and linear rigidity limitations in Mahalanobis distance metric learning for kNN classification by proposing a framework to learn multiple metrics in closed-form, resulting in a more economical approach.
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.