Modified Large Margin Nearest Neighbor Metric Learning for Regression
Data(s) |
16/02/2016
31/12/1969
16/02/2016
01/03/2014
|
---|---|
Resumo |
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. IRSC / CIHR |
Identificador |
Assi KC, Labelle H, Cheriet F. Modified Large Margin Nearest Neighbor Metric Learning for Regression. IEEE Signal Processing Letters. 2014;21(3):292-6. http://hdl.handle.net/1866/13078 10.1109/LSP.2014.2301037 |
Idioma(s) |
en |
Publicador |
IEEE |
Relação |
IEEE Signal Processing Letters;21(3) |
Palavras-Chave | #3-D shape prediction Mahalanobis distance metric learning nearest neighbor regression semidefinite programming |
Tipo |
Article |