TY - GEN
T1 - Locality-sensitive binary codes from shift-invariant kernels
AU - Raginsky, Maxim
AU - Lazebnik, Svetlana
PY - 2009
Y1 - 2009
N2 - This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel (e.g., a Gaussian kernel) between the vectors. We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent state-of-the-art method, spectral hashing.
AB - This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel (e.g., a Gaussian kernel) between the vectors. We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent state-of-the-art method, spectral hashing.
UR - http://www.scopus.com/inward/record.url?scp=79954525255&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:79954525255
SN - 9781615679119
T3 - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
SP - 1509
EP - 1517
BT - Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
PB - Neural Information Processing Systems
T2 - 23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
Y2 - 7 December 2009 through 10 December 2009
ER -