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Hashing has proven a valuable tool for large-scale information retrieval.Despite much success, existing hashing methods optimize over simple objectivessuch as the reconstruction error or graph Laplacian related loss functions,instead of the performance evaluation criteria of interest---multivariateperformance measures such as the AUC and NDCG. Here we present a generalframework (termed StructHash) that allows one to directly optimize multivariateperformance measures. The resulting optimization problem can involveexponentially or infinitely many variables and constraints, which is morechallenging than standard structured output learning. To solve the StructHashoptimization problem, we use a combination of column generation andcutting-plane techniques. We demonstrate the generality of StructHash byapplying it to ranking prediction and image retrieval, and show that itoutperforms a few state-of-the-art hashing methods.

Optimizing Ranking Measures for Compact Binary Code Learning