Metric learning by directly minimizing the k-NN training error

Konstantin Chernoff; Marco Loog; Mads Nielsen

Metric learning by directly minimizing the k-NN training error

Konstantin Chernoff, Marco Loog, Mads Nielsen

Datalogisk Institut

1 Citationer (Scopus)

Abstract

This paper presents an approach for computing global distance metrics that minimize the k-NN leave-one-out (LOO) error. The approach optimizes an energy function that corresponds to a smoothened version of the k-NN LOO error. The generalization of the proposed approach is further improved by controlling the k parameter through a heuristic. Evaluation of the proposed approach on several public datasets showed that it was able to compete with an established state-of-the art approach.

Originalsprog	Engelsk
Titel	2012 21st International Conference on Pattern Recognition (ICPR)
Antal sider	4
Forlag	IEEE
Publikationsdato	2012
Sider	1265-1268
ISBN (Trykt)	978-1-4673-2216-4
Status	Udgivet - 2012
Begivenhed	21st International Conference on Pattern Recognition - Tsukuba Science City , Japan Varighed: 11 nov. 2012 → 15 nov. 2012 Konferencens nummer: 21

Konference

Konference	21st International Conference on Pattern Recognition
Nummer	21
Land/Område	Japan
By	Tsukuba Science City
Periode	11/11/2012 → 15/11/2012

Emneord

learning (artificial intelligence)
LOO error minimization
energy function
global distance metrics
k-NN training error minimization
leave-one-out error minimization
metric learning
public datasets
Art
Iris
Machine learning
Measurement
Optimization
Training
Vectors

Adgang til dokumentet

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6460369&queryText%3DMetric+learning+by+directly+minimizing+the+k-NN+training+error

Citationsformater

Chernoff, K, Loog, M & Nielsen, M 2012, Metric learning by directly minimizing the k-NN training error. i 2012 21st International Conference on Pattern Recognition (ICPR). IEEE, s. 1265-1268, 21st International Conference on Pattern Recognition , Tsukuba Science City , Japan, 11/11/2012. <http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6460369&queryText%3DMetric+learning+by+directly+minimizing+the+k-NN+training+error>

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AB - This paper presents an approach for computing global distance metrics that minimize the k-NN leave-one-out (LOO) error. The approach optimizes an energy function that corresponds to a smoothened version of the k-NN LOO error. The generalization of the proposed approach is further improved by controlling the k parameter through a heuristic. Evaluation of the proposed approach on several public datasets showed that it was able to compete with an established state-of-the art approach.

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KW - public datasets

KW - Art

KW - Iris

KW - Machine learning

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KW - Optimization

KW - Training

KW - Vectors

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