Huang, Zi; Hengtao, Shen; Zhou, Xiaofang; Song, Dawei and Rüger, Stefan
(2007).
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| DOI (Digital Object Identifier) Link: | https://doi.org/10.1145/1277741.1277940 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
Dimensionality reduction plays an important role in efficient similarity search, which is often based on k-nearest neighbor (k-NN) queries over a high-dimensional feature space. In this paper, we introduce a novel type of k-NN query, namely conditional k-NN (ck-NN), which considers dimension-specific constraint in addition to the inter-point distances. However, existing dimensionality reduction methods are not applicable to this new type of queries. We propose a novel Mean-Std (standard deviation) guided Dimensionality Reduction (MSDR) to support a pruning based efficient ck-NN query processing strategy. Our preliminary experimental results on 3D protein structure data demonstrate that the MSDR method is promising.
| Item Type: | Conference or Workshop Item |
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| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications Faculty of Science, Technology, Engineering and Mathematics (STEM) Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) |
| Item ID: | 11971 |
| Depositing User: | Users 8580 not found. |
| Date Deposited: | 09 Oct 2008 12:58 |
| Last Modified: | 26 May 2018 07:01 |
| URI: | http://oro.open.ac.uk/id/eprint/11971 |
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