| Approximate nearest subspace search. | |
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MedLine Citation:
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PMID: 20513927 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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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. |
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Authors:
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Ronen Basri; Tal Hassner; Lihi Zelnik-Manor |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: IEEE transactions on pattern analysis and machine intelligence Volume: 33 ISSN: 1939-3539 ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Publication Date: 2011 Feb |
Date Detail:
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Created Date: 2011-04-20 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9885960 Medline TA: IEEE Trans Pattern Anal Mach Intell Country: United States |
Other Details:
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Languages: eng Pagination: 266-78 Citation Subset: IM |
Affiliation:
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Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. ronen.basri@weizmann.ac.il |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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