Search results for key=ZSS2010a : 1 match found.

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.},
  }