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Summary: Boston University Computer Science Tech. Report No. 2005-010, March 21, 2005.
To appear in Proceedings of ACM International Conference on Management of Data (SIGMOD), June 2005.
Query-Sensitive Embeddings
Vassilis Athitsos Marios Hadjieleftheriou George Kollios Stan Sclaroff
Computer Science Department
Boston University
111 Cummington Street
Boston, MA 02215, USA
{athitsos,marioh,gkollios,sclaroff}@cs.bu.edu
ABSTRACT
A common problem in many types of databases is retrieving
the most similar matches to a query object. Finding those
matches in a large database can be too slow to be practi-
cal, especially in domains where objects are compared us-
ing computationally expensive similarity (or distance) mea-
sures. This paper proposes a novel method for approxi-
mate nearest neighbor retrieval in such spaces. Our method
is embedding-based, meaning that it constructs a function
that maps objects into a real vector space. The mapping
preserves a large amount of the proximity structure of the
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