文献类型：期刊文献

英文题名：Learning large margin nearest neighbor classifiers via cutting plane algorithm

作者：Qing, Xiang-Yun[1]; Ding, Peng[1]; Wang, Xing-Yu[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

年份：2010

卷号：1

起止页码：241

外文期刊名：2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

收录：EI(收录号：20104613374996)

语种：英文

外文关键词：Artificial intelligence - Nearest neighbor search - Motion compensation - Pattern recognition

摘要：The performance of popular and classical k-nearest neighbor classifier depends on the distance metric. Large margin nearest neighbor classifier using gradient optimization method is prone to local minima. In this paper, we present a Mahalanobis metric learning method based on cutting plane algorithm which reduces largely constraints for solving the semidefinite programming problem. Experimental results on the DCI data sets show that our method can achieve promising speedups compared with the gradient based method under the similar training, test error rates. ? 2010 IEEE.