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  • @inproceedings{ZSSa2010,
    	vgclass =	{refpap},
    	author =	{Zaidi, Nayyar Abbas and David McG.\ Squire and David
    	title =	{A Gradient-based Metric Learning Algorithm for {k-NN}
    	booktitle =	{Proceedings of the 23rd Australasian Joint Conference on
    	Artificial Intelligence},
    	address =	{Adelaide, Australia},
    	number =	{6464},
    	series =	{Lecture Notes in Computer Science},
    	pages =	{194--203},
    	publisher =	{Springer-Verlag},
    	month =	{December~7--10},
    	year =	{2010},
    	doi =	{},
    	abstract =	{The Nearest Neighbor (NN) classification/regression
    	techniques, besides their simplicity, are amongst the most widely
    	applied and well studied techniques for pattern recognition in machine
    	learning. A drawback, however, is the assumption of the availability of
    	a suitable metric to measure distances to the $k$ nearest neighbors. It
    	has been shown that k-NN classifiers with a suitable distance metric
    	can perform better than other, more sophisticated, alternatives such as
    	Support Vector Machines and Gaussian Process classifiers.  For this
    	reason, much recent research in k-NN methods has focused on metric
    	learning, i.e.\ finding an optimized metric. In this paper we propose 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 differs from the prevalent
    	techniques in metric learning, where the 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 results in greatly improved k-NN performance, but
    	also performs better than competing metric learning techniques.
    	Promising results are reported on major UCIML databases.},