Modified Large Margin Nearest Neighbor Metric Learning for Regression
dc.contributor.author | Assi, Kondo C. | |
dc.contributor.author | Labelle, Hubert | |
dc.contributor.author | Cheriet, Farida | |
dc.date.accessioned | 2016-02-16T16:22:12Z | |
dc.date.available | NO_RESTRICTION | fr |
dc.date.available | 2016-02-16T16:22:12Z | |
dc.date.issued | 2014-03 | |
dc.identifier.uri | http://hdl.handle.net/1866/13078 | |
dc.description.sponsorship | IRSC / CIHR | fr |
dc.publisher | IEEE | fr |
dc.subject | 3-D shape prediction Mahalanobis distance metric learning nearest neighbor regression semidefinite programming | fr |
dc.title | Modified Large Margin Nearest Neighbor Metric Learning for Regression | fr |
dc.type | Article | fr |
dc.contributor.affiliation | Université de Montréal. Faculté de médecine. Département de chirurgie | fr |
dc.identifier.doi | 10.1109/LSP.2014.2301037 | |
dcterms.abstract | 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. | fr |
dcterms.language | eng | fr |
UdeM.VersionRioxx | Version acceptée / Accepted Manuscript | |
oaire.citationTitle | IEEE signal processing letters | |
oaire.citationVolume | 21 | |
oaire.citationIssue | 3 | |
oaire.citationStartPage | 292 | |
oaire.citationEndPage | 296 |
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