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Sequential Projection Learning for Hashing with Compact Codes
International Conference on Machine Learning
Hashing based Approximate Nearest Neighbor (ANN) search has attracted much attention due to its fast query time and drastically reduced storage. However, most of the hashing methods either use random projections or extract principal directions from the data to derive hash functions. The resulting embedding suffers from poor discrimination when compact codes are used. In this paper, we propose a novel data-dependent projection learning method such that each hash function is designed to correctdblp:conf/icml/WangKC10 fatcat:lq73yfkrenglvhnnu6kykymdiu