DocumentCode :
2488576
Title :
Fast and regularized local metric for query-based operations
Author :
Abou-Moustafa, Karim ; Ferrie, Frank
Author_Institution :
Centre for Intell. Machines, McGill Univ., Montreal, QC
fYear :
2008
fDate :
8-11 Dec. 2008
Firstpage :
1
Lastpage :
4
Abstract :
To learn a metric for query-based operations, we combine the concept underlying manifold learning algorithms and the minimum volume ellipsoid metric in a unified algorithm to find the nearest neighbouring points on the manifold on which the query point is lying. Extensive experiments on standard benchmark data sets in the context of classification showed promising and interesting results with regard to our proposed algorithm.
Keywords :
learning (artificial intelligence); minimisation; pattern classification; query processing; manifold learning algorithm; minimum volume ellipsoid metric; nearest neighbour point classifier; query-based operation; Covariance matrix; Ellipsoids; Euclidean distance; Geometry; Laboratories; Machine learning; Manifolds; Nearest neighbor searches; Noise measurement; Symmetric matrices;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
ISSN :
1051-4651
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
Type :
conf
DOI :
10.1109/ICPR.2008.4761777
Filename :
4761777
Link To Document :
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