Huang, Zi; Hengtao, Shen; Zhou, Xiaofang; Song, Dawei and Rüger, Stefan
(2007).
![[img]](http://oro.open.ac.uk/style/images/fileicons/application_pdf.png)
|
PDF (Not Set)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (209Kb) |
| DOI (Digital Object Identifier) Link: | http://doi.org/10.1145/1277741.1277940 |
|---|---|
| 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 Item |
|---|---|
| Academic Unit/Department: | 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: | 02 Aug 2016 16:31 |
| URI: | http://oro.open.ac.uk/id/eprint/11971 |
| Share this page: |     |
Altmetrics | Scopus Citations |
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.