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Online Metric Learning and Fast Similarity Search
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@MISC{Jain_onlinemetric,
author = {Prateek Jain and Brian Kulis and Inderjit S. Dhillon and Kristen Grauman},
title = {Online Metric Learning and Fast Similarity Search},
year = {}
}
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Abstract
Metric learning algorithms can provide useful distance functions for a variety of domains, and recent work has shown good accuracy for problems where the learner can access all distance constraints at once. However, in many real applications, constraints are only available incrementally, thus necessitating methods that can perform online updates to the learned metric. Existing online algorithms offer bounds on worst-case performance, but typically do not perform well in practice as compared to their offline counterparts. We present a new online metric learning algorithm that updates a learned Mahalanobis metric based on LogDet regularization and gradient descent. We prove theoretical worst-case performance bounds, and empirically compare the proposed method against existing online metric learning algorithms. To further boost the practicality of our approach, we develop an online locality-sensitive hashing scheme which leads to efficient updates to data structures used for fast approximate similarity search. We demonstrate our algorithm on multiple datasets and show that it outperforms relevant baselines. 1
Keyphrases
fast similarity search online metric learning many real application data structure online algorithm offer online locality-sensitive hashing scheme online update new online theoretical worst-case performance bound multiple datasets offline counterpart fast approximate similarity search gradient descent logdet regularization learned mahalanobis distance constraint worst-case performance recent work useful distance function relevant baseline good accuracy
