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.
Vassilis Athitsos Marios Hadjieleftheriou George Kollios Stan Sclaroff
Computer Science Department
111 Cummington Street
Boston, MA 02215, USA
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