A Centroid k-Nearest Neighbor Method

摘要：

k-nearest neighbor method (kNN) is a very useful and easy-implementing method for real applications. The query point is estimated by its k nearest neighbors. However, this kind of prediction simply uses the label information of its neighbors without considering their space distributions. This paper proposes a novel kNN method in which the centroids instead of the neighbors themselves are employed. The cen-troids can reflect not only the label information but also the distribution information of its neighbors. In order to evaluate the proposed method, Euclidean distance and Mahalanobis distance is used in our experiments. Moreover, traditional kNN is also implemented to provide a comparison with the proposed method. The empirical results suggest that the propose method is more robust and effective.

关键词： Distance metric learning k-nearest neighbor Euclidean `distance Mahalanobis distance

作者: Qingjiu Zhang Shiliang Sun

作者单位: Department of Computer Science and Technology East China Normal University,500 Dongchuan Road Shanghai 200241 P.R. China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 278-285

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）