The Open UniversitySkip to content
 

Dimensionality reduction for dimension-specific search

Huang, Zi; Hengtao, Shen; Zhou, Xiaofang; Song, Dawei and Rüger, Stefan (2007). Dimensionality reduction for dimension-specific search. In: 30th Annual International ACM SIGIR conference on Research and Development in information retrieval, 23-27 July 2007, Amsterdam, The Netherlands, p. 849.

Full text available as:
[img]
Preview
PDF (Not Set) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (209Kb)
DOI (Digital Object Identifier) Link: http://dx.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: Mathematics, Computing and Technology > Computing & Communications
Knowledge Media Institute
Item ID: 11971
Depositing User: Users 8580 not found.
Date Deposited: 09 Oct 2008 12:58
Last Modified: 23 Jun 2012 06:09
URI: http://oro.open.ac.uk/id/eprint/11971
Share this page:

Actions (login may be required)

View Item
Report issue / request change

Policies | Disclaimer

© The Open University   + 44 (0)870 333 4340   general-enquiries@open.ac.uk