Efficiently learning a distance metric for large margin nearest neighbor classification
We concern the problem of learning a Mahalanobis distance metric for improving nearest neighbor classification. Our work is built upon the large margin nearest neighbor (LMNN) classification framework. Due to the semidefiniteness constraint in the optimization problem of LMNN, it is not scalable in terms of the dimensionality of the input data. The original LMNN solver partially alleviates this problem by adopting alternating projection methods instead of standard interior-point methods. Still,...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of AAAI 2011|
|01_Park_Efficiently_learning_a_2011.pdf||533.17 kB||Adobe PDF||Request a copy|
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