Presentation | 2001/12/14 Speed-up of kNN Search and kNN Classification by Filtering Based on Dimensionality Reduction Kohei INOUE, Kiichi URAHAMA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Acceleration methods are presented for k nearest neighbor (NN) search and kNN classification on the basis of dimensionality reduction of feature vectors. Upper and lower bounds of distance between data are derived for general norm. They include distance in low dimension. Computational costs of kNN search and classification are reduced by filtering data on the basis of the inequality of distance. As an example of high dimensional data, color histograms of images are examined. Experiments show that the filtering with both upper and lower bounds is faster than that with only lower bound, and kNN classification which utilizes properties of classification is faster than that utilizes kNN search simply. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | kNN search / kNN classification / upper and lower bounds of distance / filtering |
Paper # | PRMU2001-176 |
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Committee | PRMU |
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Conference Date | 2001/12/14(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Speed-up of kNN Search and kNN Classification by Filtering Based on Dimensionality Reduction |
Sub Title (in English) | |
Keyword(1) | kNN search |
Keyword(2) | kNN classification |
Keyword(3) | upper and lower bounds of distance |
Keyword(4) | filtering |
1st Author's Name | Kohei INOUE |
1st Author's Affiliation | Faculty of Visual communication Design, Kyushu Institute of Design() |
2nd Author's Name | Kiichi URAHAMA |
2nd Author's Affiliation | Faculty of Visual communication Design, Kyushu Institute of Design |
Date | 2001/12/14 |
Paper # | PRMU2001-176 |
Volume (vol) | vol.101 |
Number (no) | 525 |
Page | pp.pp.- |
#Pages | 6 |
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