Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Efficiently Monitoring Nearest Neighbors to a Moving Object
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Reverse k-nearest neighbor search in dynamic and general metric databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Local within-class accuracies for weighting individual outputs in multiple classifier systems
Pattern Recognition Letters
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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 centroids 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.