@conference {37,
	title = {Reduced-Rank Local Distance Metric Learning},
	booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2013},
	year = {2013},
	note = {<p>Acceptance rate 25\% (111/443).</p>
},
	publisher = {Springer},
	organization = {Springer},
	address = {Prague, Czech Republic},
	abstract = {<p>We propose a new method for local metric learning based on a conical combination of Mahalanobis metrics and pair-wise similarities between the data. Its formulation allows for controlling the rank of the metrics{\textquoteright} weight matrices. We also offer a convergent algorithm for training the associated model. Experimental results on a collection of classification problems imply that the new method may offer notable performance advantages over alternative metric learning approaches that have recently appeared in the literature.</p>
},
	doi = {10.1007/978-3-642-40994-3_15},
	author = {Yinjie Huang and Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos}
}
