Sequential Projection Learning for Hashing with Compact Codes

Jun Wang, Sanjiv Kumar, Shih-Fu Chang
2010 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 correct
more » ... e errors made by the previous one sequentially. The proposed method easily adapts to both unsupervised and semi-supervised scenarios and shows significant performance gains over the state-ofthe-art methods on two large datasets containing up to 1 million points.
dblp:conf/icml/WangKC10 fatcat:lq73yfkrenglvhnnu6kykymdiu