Spectral hashing

Yair Weiss*, Antonio Torralba, Rob Fergus

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2041 Scopus citations

Abstract

Semantic hashing[1] seeks compact binary codes of data-points so that the Hamming distance between codewords correlates with semantic similarity. In this paper, we show that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard. By relaxing the original problem, we obtain a spectral method whose solutions are simply a subset of thresholded eigen- vectors of the graph Laplacian. By utilizing recent results on convergence of graph Laplacian eigenvectors to the Laplace-Beltrami eigenfunctions of manifolds, we show how to efficiently calculate the code of a novel data- point. Taken together, both learning the code and applying it to a novel point are extremely simple. Our experiments show that our codes outper- form the state-of-the art.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
PublisherNeural Information Processing Systems
Pages1753-1760
Number of pages8
ISBN (Print)9781605609492
StatePublished - 2009
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: 8 Dec 200811 Dec 2008

Publication series

NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Conference

Conference22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
Country/TerritoryCanada
CityVancouver, BC
Period8/12/0811/12/08

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