Efficient similarity search within user-specified projective subspaces

Document Type

Article

Publication Date

7-1-2016

Abstract

Many applications - such as content-based image retrieval, subspace clustering, and feature selection - may benefit from efficient subspace similarity search. Given a query object, the goal of subspace similarity search is to retrieve the most similar objects from the database, where the similarity distance is defined over an arbitrary subset of dimensions (or features) - that is, an arbitrary axis-aligned projective subspace - specified along with the query. Though much effort has been spent on similarity search in fixed subspaces, relatively little attention has been given to the problem of similarity search when the dimensions are specified at query time. In this paper, we propose new methods for the subspace similarity search problem for real-valued data. Extensive experiments are provided showing very competitive performance relative to state-of-the-art solutions.

Identifier

84959515222 (Scopus)

Publication Title

Information Systems

External Full Text Location

https://doi.org/10.1016/j.is.2016.01.008

ISSN

03064379

First Page

2

Last Page

14

Volume

59

Grant

1241976

Fund Ref

National Science Foundation

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