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Distance metric learning is a successful way to enhance the performance ofthe nearest neighbor classifier. In most cases, however, the distribution ofdata does not obey a regular form and may change in different parts of thefeature space. Regarding that, this paper proposes a novel local distancemetric learning method, namely Local Mahalanobis Distance Learning (LMDL), inorder to enhance the performance of the nearest neighbor classifier. LMDLconsiders the neighborhood influence and learns multiple distance metrics for areduced set of input samples. The reduced set is called as prototypes which tryto preserve local discriminative information as much as possible. The proposedLMDL can be kernelized very easily, which is significantly desirable in thecase of highly nonlinear data. The quality as well as the efficiency of theproposed method assesses through a set of different experiments on variousdatasets and the obtained results show that LDML as well as the kernelizedversion is superior to the other related state-of-the-art methods.

Local Distance Metric Learning for Nearest Neighbor Algorithm