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SIGMOD
1998
ACM

Dimensionality Reduction for Similarity Searching in Dynamic Databases

11 years 11 months ago
Dimensionality Reduction for Similarity Searching in Dynamic Databases
Databases are increasingly being used to store multi-media objects such as maps, images, audio and video. Storage and retrieval of these objects is accomplished using multi-dimensional index structures such as R¢ -trees and SS-trees. As dimensionality increases, query performance in these index structures degrades. This phenomenon, generally referred to as the dimensionality curse, can be circumvented by reducing the dimensionality of the data. Such a reduction is however accompanied by a loss of precision of query results. Current techniques such as QBIC use SVD transform-based dimensionality reduction to ensure high query precision. The drawback of this approach is that SVD is expensive to compute, and therefore not readily applicable to dynamic databases. In this paper, we propose novel techniques for performing SVD-based dimensionality reduction in dynamic databases. When the data distribution changes considerably so as to degrade query precision, we recompute the SVD transform a...
Kothuri Venkata Ravi Kanth, Divyakant Agrawal, Amb
Added 05 Aug 2010
Updated 05 Aug 2010
Type Conference
Year 1998
Where SIGMOD
Authors Kothuri Venkata Ravi Kanth, Divyakant Agrawal, Ambuj K. Singh
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