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Unsupervised methods have been recently used to produce more compact hashes than their randomized equivalents. The authors demonstrate that existing methods suffer from bad performance as the length of the codes increases and suggest a new graph-based method. To achieve better codes, they keep the binary constraints and consider a slightly relaxed formulation (still NP-hard) that they solve using alternating maximization. Local convergence is guaranteed and extensive experiments show that the suggested method achieves very good performance on a number of datasets.