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