In this paper, we propose a novel method to learn similarity preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the original space and the ones in the hamming space. The problem of mapping the NN points into different hash codes is taken as a classification problem in which the points are categorized into several groups according to the hamming distances to the query. The hash functions are optimized from the classifiers pooled over the training points. Experimental results demonstrate the superiority of our approach over existing state-of-the-art hashing techniques.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we propose a novel method to learn similarity preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the original space and the ones in the hamming space. 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