Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Avoiding confusing features in place recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Adapting visual category models to new domains
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Semi-supervised SimHash for efficient document similarity search
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Pick your neighborhood: improving labels and neighborhood structure for label propagation
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Metric and kernel learning using a linear transformation
The Journal of Machine Learning Research
Manhattan hashing for large-scale image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Robust re-identification using randomness and statistical learning: Quo vadis
Pattern Recognition Letters
Similar image search with a tiny bag-of-delegates representation
Proceedings of the 20th ACM international conference on Multimedia
Image retrieval with query-adaptive hashing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Comparing apples to oranges: a scalable solution with heterogeneous hashing
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Smart hashing update for fast response
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality makes it infeasible to learn an explicit transformation over the feature dimensions. We demonstrate the approach applied to a variety of image data sets, as well as a systems data set. The learned metrics improve accuracy relative to commonly used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.