Efficient algorithms for similarity search in axis-aligned subspaces

Michael E. Houle, Xiguo Ma, Vincent Oria, Jichao Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

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. 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 several new methods for the subspace similarity search problem. Extensive experiments are provided showing very competitive performance relative to state-of-the-art solutions.

Original languageEnglish (US)
Title of host publicationSimilarity Search and Applications - 7th International Conference, SISAP 2014, Proceedings
EditorsAgma Juci Machado Traina, Caetano Traina, Robson Leonardo Ferreira Cordeiro
PublisherSpringer Verlag
Pages1-12
Number of pages12
ISBN (Electronic)9783319119878
DOIs
StatePublished - 2014
Event7th International Conference on Similarity Search and Applications, SISAP 2014 - Los Cabos, Mexico
Duration: Oct 29 2014Oct 31 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8821
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Conference on Similarity Search and Applications, SISAP 2014
CountryMexico
CityLos Cabos
Period10/29/1410/31/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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