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Reduced-Rank Local Distance Metric Learning
|Title||Reduced-Rank Local Distance Metric Learning|
|Publication Type||Conference Paper|
|Year of Publication||2013|
|Authors||Huang Y, Li C, Georgiopoulos M, Anagnostopoulos GC|
|Conference Name||Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2013|
|Conference Location||Prague, Czech Republic|
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’ 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.
Acceptance rate 25% (111/443).