Locality-Sensitive Binary Codes from Shift-Invariant Kernels
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.
Cite
Text
Raginsky and Lazebnik. "Locality-Sensitive Binary Codes from Shift-Invariant Kernels." Neural Information Processing Systems, 2009.Markdown
[Raginsky and Lazebnik. "Locality-Sensitive Binary Codes from Shift-Invariant Kernels." Neural Information Processing Systems, 2009.](https://mlanthology.org/neurips/2009/raginsky2009neurips-localitysensitive/)BibTeX
@inproceedings{raginsky2009neurips-localitysensitive,
title = {{Locality-Sensitive Binary Codes from Shift-Invariant Kernels}},
author = {Raginsky, Maxim and Lazebnik, Svetlana},
booktitle = {Neural Information Processing Systems},
year = {2009},
pages = {1509-1517},
url = {https://mlanthology.org/neurips/2009/raginsky2009neurips-localitysensitive/}
}