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Probably correct k-nearest neighbor search in high dimensions
| Title: | Probably correct k-nearest neighbor search in high dimensions |
| Authors: | Toyama, Jun Browse this author | | Kudo, Mineichi Browse this author →KAKEN DB | | Imai, Hideyuki Browse this author |
| Keywords: | Pattern recognition | | The k-nearest neighbor method | | Probably correct algorithm | | PAC framework |
| Issue Date: | Apr-2010 |
| Publisher: | Elsevier |
| Journal Title: | Pattern Recognition |
| Volume: | 43 |
| Issue: | 4 |
| Start Page: | 1361 |
| End Page: | 1372 |
| Publisher DOI: | 10.1016/j.patcog.2009.09.026 |
| Abstract: | A novel approach for k-nearest neighbor (k-NN) searching with Euclidean metric is described. It is well known that many sophisticated algorithms cannot beat the brute-force algorithm when the dimensionality is high. In this study, a probably correct approach, in which the correct set of k-nearest neighbors is obtained in high probability, is proposed for greatly reducing the searching time. We exploit the marginal distribution of the kth nearest neighbors in low dimensions, which is estimated from the stored data (an empirical percentile approach). We analyze the basic nature of the marginal distribution and show the advantage of the implemented algorithm, which is a probabilistic variant of the partial distance searching. Its query time is sublinear in data size n, that is, O(mnδ) with δ=o(1) in n and δ ≤ 1, for any fixed dimension m. |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/42786 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 工藤 峰一
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