Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/479244
Title: Approximate nearest subspace search
Authors: Basri, Ronen;Hassner, Tal;Zelnik-Manor, Lihi
subject: Approximate nearest neighbor search techniques;subspace representations.
Year: 2011
Abstract: Subspaces offer convenient means of representing information in many pattern recognition, machine vision, and statistical learning applications. Contrary to the growing popularity of subspace representations, the problem of efficiently searching through large subspace databases has received little attention in the past. In this paper, we present a general solution to the problem of Approximate Nearest Subspace search. Our solution uniformly handles cases where the queries are points or subspaces, where query and database elements differ in dimensionality, and where the database contains subspaces of different dimensions. To this end, we present a simple mapping from subspaces to points, thus reducing the problem to the well-studied Approximate Nearest Neighbor problem on points. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments indicate that an approximate nearest subspace can be located significantly faster than the nearest subspace, with little loss of accuracy.
Description: 
URI: http://localhost/handle/Hannan/284125
http://localhost/handle/Hannan/479244
ISSN: 9781424444427
Appears in Collections:2011

Files in This Item:
File SizeFormat 
AL1935846.pdf1.83 MBAdobe PDF
Title: Approximate nearest subspace search
Authors: Basri, Ronen;Hassner, Tal;Zelnik-Manor, Lihi
subject: Approximate nearest neighbor search techniques;subspace representations.
Year: 2011
Abstract: Subspaces offer convenient means of representing information in many pattern recognition, machine vision, and statistical learning applications. Contrary to the growing popularity of subspace representations, the problem of efficiently searching through large subspace databases has received little attention in the past. In this paper, we present a general solution to the problem of Approximate Nearest Subspace search. Our solution uniformly handles cases where the queries are points or subspaces, where query and database elements differ in dimensionality, and where the database contains subspaces of different dimensions. To this end, we present a simple mapping from subspaces to points, thus reducing the problem to the well-studied Approximate Nearest Neighbor problem on points. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments indicate that an approximate nearest subspace can be located significantly faster than the nearest subspace, with little loss of accuracy.
Description: 
URI: http://localhost/handle/Hannan/284125
http://localhost/handle/Hannan/479244
ISSN: 9781424444427
Appears in Collections:2011

Files in This Item:
File SizeFormat 
AL1935846.pdf1.83 MBAdobe PDF
Title: Approximate nearest subspace search
Authors: Basri, Ronen;Hassner, Tal;Zelnik-Manor, Lihi
subject: Approximate nearest neighbor search techniques;subspace representations.
Year: 2011
Abstract: Subspaces offer convenient means of representing information in many pattern recognition, machine vision, and statistical learning applications. Contrary to the growing popularity of subspace representations, the problem of efficiently searching through large subspace databases has received little attention in the past. In this paper, we present a general solution to the problem of Approximate Nearest Subspace search. Our solution uniformly handles cases where the queries are points or subspaces, where query and database elements differ in dimensionality, and where the database contains subspaces of different dimensions. To this end, we present a simple mapping from subspaces to points, thus reducing the problem to the well-studied Approximate Nearest Neighbor problem on points. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments indicate that an approximate nearest subspace can be located significantly faster than the nearest subspace, with little loss of accuracy.
Description: 
URI: http://localhost/handle/Hannan/284125
http://localhost/handle/Hannan/479244
ISSN: 9781424444427
Appears in Collections:2011

Files in This Item:
File SizeFormat 
AL1935846.pdf1.83 MBAdobe PDF