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Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang. Hashing with Graphs. In International Conference on Machine Learning (ICML), Bellevue, WA, USA, 2011.

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Hashing is becoming increasingly popular for efficient nearest neighbor search in massive databases. However, learning short codes that yield good search performance is still a challenge. Moreover, in many cases real world data lives on a low-dimensional manifold, which should be taken into account to capture meaningful nearest neighbors. In this paper, we propose a novel graph-based hashing method which automatically discovers the neighborhood structure inherent in the data to learn appropriate compact codes. To make such an approach computationally feasible, we utilize Anchor Graphs to obtain tractable low-rank adjacency matrices. Our formulation allows constant time hashing of a new data point by extrapolating graph Laplacian eigenvectors to eigenfunctions. Finally, we describe a hierarchical threshold learning procedure in which each eigenfunction yields multiple bits, leading to higher search accuracy. Experimental comparison with the other state-of-the-art methods on two large datasets demonstrates the efficacy of the proposed method


Wei Liu
Jun Wang
Shih-Fu Chang

BibTex Reference

   Author = {Liu, Wei and Wang, Jun and Kumar, Sanjiv and Chang, Shih-Fu},
   Title = {Hashing with Graphs},
   BookTitle = {International Conference on Machine Learning (ICML)},
   Address = {Bellevue, WA, USA},
   Year = {2011}

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