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Fast Similarity Search for Learned Metrics
2009
IEEE Transactions on Pattern Analysis and Machine Intelligence
We propose a method to efficiently index into a large database of examples according to a learned metric. Given a collection of examples, we learn a Mahalanobis distance using an information-theoretic metric learning technique that adapts prior knowledge about pairwise distances to incorporate similarity and dissimilarity constraints. To enable sub-linear time similarity search under the learned metric, we show how to encode a learned Mahalanobis parameterization into randomized
doi:10.1109/tpami.2009.151
pmid:19834137
fatcat:hxm7popjxzfj3gpukzn43jufau