Applying neighborhood consistency for fast clustering and kernel density estimation

@article{Zhang2005ApplyingNC,
  title={Applying neighborhood consistency for fast clustering and kernel density estimation},
  author={Kai Zhang and Ming Tang and James T. Kwok},
  journal={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  year={2005},
  volume={2},
  pages={1001-1007 vol. 2}
}
Nearest neighborhood consistency is an important concept in statistical pattern recognition, which underlies the well-known k-nearest neighbor method. In this paper, we combine this idea with kernel density estimation based clustering, and derive the fast mean shift algorithm (FMS). FMS greatly reduces the complexity of feature space analysis, resulting satisfactory precision of classification. More importantly, we show that with FMS algorithm, we are in fact relying on a conceptually novel… CONTINUE READING
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