Hashing with Graphs

Hashing with Graphs

2011 | Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang
This paper introduces a novel graph-based hashing method called Anchor Graph Hashing (AGH) that automatically discovers the neighborhood structure of data to learn compact codes for efficient nearest neighbor search. AGH uses *Anchor Graphs* to approximate the neighborhood graph, which significantly reduces the computational complexity compared to traditional methods. The method learns hash functions by thresholding the lower eigenfunctions of the Anchor Graph Laplacian in a hierarchical manner, allowing for constant-time hashing of new data points. The proposed method is evaluated on two large datasets, MNIST and NUS-WIDE, and compared with other state-of-the-art hashing methods. Experimental results demonstrate that AGH outperforms existing methods in terms of both precision and recall, making it a scalable and effective approach for nearest neighbor search in high-dimensional spaces.This paper introduces a novel graph-based hashing method called Anchor Graph Hashing (AGH) that automatically discovers the neighborhood structure of data to learn compact codes for efficient nearest neighbor search. AGH uses *Anchor Graphs* to approximate the neighborhood graph, which significantly reduces the computational complexity compared to traditional methods. The method learns hash functions by thresholding the lower eigenfunctions of the Anchor Graph Laplacian in a hierarchical manner, allowing for constant-time hashing of new data points. The proposed method is evaluated on two large datasets, MNIST and NUS-WIDE, and compared with other state-of-the-art hashing methods. Experimental results demonstrate that AGH outperforms existing methods in terms of both precision and recall, making it a scalable and effective approach for nearest neighbor search in high-dimensional spaces.
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