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Summary: Query-Sensitive Embeddings
VASSILIS ATHITSOS
Boston University
MARIOS HADJIELEFTHERIOU
AT&T Labs-Research
GEORGE KOLLIOS
Boston University
and
STAN SCLAROFF
Boston University
A common problem in many types of databases is retrieving the most similar matches to a query ob-
ject. Finding these matches in a large database can be too slow to be practical, especially in domains
where objects are compared using computationally expensive similarity (or distance) measures.
Embedding methods can significantly speed-up retrieval by mapping objects into a vector space,
where distances can be measured rapidly using a Minkowski metric. In this article we present a
novel way to improve embedding quality. In particular, we propose to construct embeddings that
use a query-sensitive distance measure for the target space of the embedding. This distance mea-
sure is used to compare those vectors that the query and database objects are mapped to. The
term "query-sensitive" means that the distance measure changes, depending on the current query
object. We demonstrate theoretically that using a query-sensitive distance measure increases the
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