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Locality-Sensitive Binary Codes from Shift-Invariant Kernels
This video was recorded at 23rd Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2009. 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
