@inproceedings{d5b0cf0e5731481cadf232e1b9d947c5,
title = "Classification with mixtures of curved mahalanobis metrics",
abstract = "We study the classification with respect to the class of curved Mahalanobis metrics that extend the celebrated flat Mahalanobis distances to constant curvature spaces. We prove that these curved Mahalanobis k-NN classifiers define piecewise linear decision boundaries, and report the performance of learning those metrics within the framework of the Large Margin Nearest Neighbor (LMNN). Finally, we show experimentally that a mixture of curved Mahalanobis metrics define a composite metric distance that improves the classification performance.",
keywords = "Cayley-Klein geometry, Classification, Large Margin Nearest Neighbor (LMNN), Mahalanobis distance, Metric learning",
author = "Frank Nielsen and Boris Muzellec and Richard Nock",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd IEEE International Conference on Image Processing, ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",
year = "2016",
month = aug,
day = "3",
doi = "10.1109/ICIP.2016.7532355",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "241--245",
booktitle = "2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings",
address = "United States",
}