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2006
Springer

Adaptive Nearest Neighbor Classifier Based on Supervised Ellipsoid Clustering

10 years 7 months ago
Adaptive Nearest Neighbor Classifier Based on Supervised Ellipsoid Clustering
Nearest neighbor classifier is a widely-used effective method for multi-class problems. However, it suffers from the problem of the curse of dimensionality in high dimensional space. To solve this problem, many adaptive nearest neighbor classifiers were proposed. In this paper, a locally adaptive nearest neighbor classification method based on supervised learning style which works well for the multi-classification problems is proposed. In this method, the ellipsoid clustering learning is applied to estimate an effective metric. This metric is then used in the K-NN classification. Finally, the experimental results show that it is an efficient and robust approach for multi-classification.
Guo-Jun Zhang, Ji-Xiang Du, De-Shuang Huang, Tat-M
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where FSKD
Authors Guo-Jun Zhang, Ji-Xiang Du, De-Shuang Huang, Tat-Ming Lok, Michael R. Lyu
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