Scalable Large-Margin Mahalanobis Distance Metric Learning
For many machine learning algorithms such as k-nearest neighbor ( k-NN) classifiers and k-means clustering, often their success heavily depends on the metric used to calculate distances between different data points. An effective solution for defining such a metric is to learn it from a set of labeled training samples. In this work, we propose a fast and scalable algorithm to learn a Mahalanobis distance metric. The Mahalanobis metric can be viewed as the Euclidean distance metric on the input...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Neural Networks|
|01_Shen_Scalable_Large-Margin_2010.pdf||432.45 kB||Adobe PDF||Request a copy|
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