Cart (Loading....) | Create Account
Close category search window
 

Fast approximate similarity search in extremely high-dimensional data sets

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Houle, M.E. ; Nat. Inst. of Informatics, Tokyo, Japan ; Sakuma, J.

This paper introduces a practical index for approximate similarity queries of large multi-dimensional data sets: the spatial approximation sample hierarchy (SASH). A SASH is a multi-level structure of random samples, recursively constructed by building a SASH on a large randomly selected sample of data objects, and then connecting each remaining object to several of their approximate nearest neighbors from within the sample. Queries are processed by first locating approximate neighbors within the sample, and then using the pre-established connections to discover neighbors within the remainder of the data set. The SASH index relies on a pairwise distance measure, but otherwise makes no assumptions regarding the representation of the data. Experimental results are provided for query-by-example operations on protein sequence, image, and text data sets, including one consisting of more than 1 million vectors spanning more than 1.1 million terms - far in excess of what spatial search indices can handle efficiently. For sets of this size, the SASH can return a large proportion of the true neighbors roughly 2 orders of magnitude faster than sequential search.

Published in:

Data Engineering, 2005. ICDE 2005. Proceedings. 21st International Conference on

Date of Conference:

5-8 April 2005

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.