Locality Preserving Hashing

  title={Locality Preserving Hashing},
  author={Kang Zhao and Hongtao Lu and Jincheng Mei},
Hashing has recently attracted considerable attention for large scale similarity search. However, learning compact codes with good performance is still a challenge. In many cases, the real-world data lies on a low-dimensional manifold embedded in high-dimensional ambient space. To capture meaningful neighbors, a compact hashing representation should be able to uncover the intrinsic geometric structure of the manifold, e.g., the neighborhood relationships between subregions. Most existing… CONTINUE READING
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