Locality-sensitive binary codes from shift-invariant kernels

Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)

Bibtex Metadata Paper

Authors

Maxim Raginsky, Svetlana Lazebnik

Abstract

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.