Corpus ID: 52967847

A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice

@inproceedings{Fichtenberger2018ATE,
  title={A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice},
  author={Hendrik Fichtenberger and Dennis Rohde},
  booktitle={NeurIPS},
  year={2018}
}
In the $k$-nearest neighborhood model ($k$-NN), we are given a set of points $P$, and we shall answer queries $q$ by returning the $k$ nearest neighbors of $q$ in $P$ according to some metric. This concept is crucial in many areas of data analysis and data processing, e.g., computer vision, document retrieval and machine learning. Many $k$-NN algorithms have been published and implemented, but often the relation between parameters and accuracy of the computed $k$-NN is not explicit. We study… Expand
1 Citations
Sublinear time approximation of the cost of a metric k-nearest neighbor graph

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