Modified Large Margin Nearest Neighbor Metric Learning for Regression
Article [Accepted Manuscript]
Is part ofIEEE Signal Processing Letters ; 21(3)
- Faculté de médecine. Département de chirurgie
The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.
Assi KC, Labelle H, Cheriet F. Modified Large Margin Nearest Neighbor Metric Learning for Regression. IEEE Signal Processing Letters. 2014;21(3):292-6.