Featured Article from the December Issue

Fast Similarity Search for Learned Metrics

By Brian Kulis, Prateek Jain, and Kristen Grauman

We introduce a method that enables scalable similarity search for learned metrics. Given pairwise similarity and dissimilarity constraints between some examples, we learn a Mahalanobis distance function that captures the examples' underlying relationships well. To allow sublinear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions.

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