2010
@techreport{ZSS2010a,
vgclass = {report},
author = {Zaidi, Nayyar Abbas and David McG.\ Squire and David
Suter},
title = {A Simple Gradient-based Metric Learning Algorithm for Object
Recognition},
number = {2010/256},
institution = {Clayton School of Information Technology, Monash
University},
address = {Clayton Campus, Melbourne, 3800, Australia},
year = {2010},
url = {/publications/postscript/2010/tr-2010-256-full.pdf},
abstract = {The Nearest Neighbor (NN) classification/regression
techniques, besides their simplicity, is one of the most widely applied
and well studied techniques for pattern recognition in machine
learning. Their only drawback is the assumption of the availability of
a proper metric used to measure distances to k nearest neighbors. It
has been shown that K-NN classifier's with a right distance metric can
perform better than other sophisticated alternatives like Support
Vector Machines SVM) and Gaussian Processes (GP) classifiers. That's
why recent research in k-NN methods has focused on metric learning
i.e., finding an optimized metric. In this paper we have proposed a
simple gradient based algorithm for metric learning. We discuss in
detail the motivations behind metric learning, i.e., error minimization
and margin maximization. Our formulation is different from the
prevalent techniques in metric learning where goal is to maximize the
classifier's margin. Instead our proposed technique (MEGM) finds an
optimal metric by directly minimizing the mean square error. Our
technique not only resulted in greatly improving k-NN performance but
also performed better than competing metric learning techniques. We
also compared our algorithm's performance with that of SVM. Promising
results are reported on major faces, digits, object and UCIML
databases.},
}