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Scalable Large-Margin Mahalanobis Distance Metric Learning

Shen, Chunhua; Kim, Junae; Wang, Lei


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]

CollectionsANU Research Publications
Date published: 2010
Type: Journal article
Source: IEEE Transactions on Neural Networks
DOI: 10.1109/TNN.2010.2052630


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