Indexing very high-dimensional sparse and quasi-sparse vectors for similarity searches

Indexing very high-dimensional sparse and quasi-sparse vectors for similarity searches Similarity queries on complex objects are usually translated into searches among their feature vectors. This paper studies indexing techniques for very high-dimensional (e.g., in hundreds) vectors that are sparse or quasi-sparse, i.e., vectors each having only a small number (e.g., ten) of non-zero or significant values. Based on the R-tree, the paper introduces the xS-tree that uses lossy compression of bounding regions to guarantee a reasonable minimum fan-out within the allocated storage space for each node. In addition, the paper studies the performance and scalability of the xS-tree via experiments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Indexing very high-dimensional sparse and quasi-sparse vectors for similarity searches

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Publisher
Springer-Verlag
Copyright
Copyright © 2001 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s007780100036
Publisher site
See Article on Publisher Site

Abstract

Similarity queries on complex objects are usually translated into searches among their feature vectors. This paper studies indexing techniques for very high-dimensional (e.g., in hundreds) vectors that are sparse or quasi-sparse, i.e., vectors each having only a small number (e.g., ten) of non-zero or significant values. Based on the R-tree, the paper introduces the xS-tree that uses lossy compression of bounding regions to guarantee a reasonable minimum fan-out within the allocated storage space for each node. In addition, the paper studies the performance and scalability of the xS-tree via experiments.

Journal

The VLDB JournalSpringer Journals

Published: Apr 1, 2001

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