TY - GEN
T1 - Efficient algorithms for similarity search in axis-aligned subspaces
AU - Houle, Michael E.
AU - Ma, Xiguo
AU - Oria, Vincent
AU - Sun, Jichao
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84911122363&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911122363&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11988-5_1
DO - 10.1007/978-3-319-11988-5_1
M3 - Conference contribution
AN - SCOPUS:84911122363
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 12
BT - Similarity Search and Applications - 7th International Conference, SISAP 2014, Proceedings
A2 - Traina, Agma Juci Machado
A2 - Traina, Caetano
A2 - Cordeiro, Robson Leonardo Ferreira
PB - Springer Verlag
T2 - 7th International Conference on Similarity Search and Applications, SISAP 2014
Y2 - 29 October 2014 through 31 October 2014
ER -