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Principal Component Hashing: An Accelerated Approximate Nearest Neighbor Search

  • Yusuke Matsushita
  • Toshikazu Wada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

Nearest Neighbor (NN) search is a basic algorithm for data mining and machine learning applications. However, its acceleration in high dimensional space is a difficult problem. For solving this problem, approximate NN search algorithms have been investigated. Especially, LSH is getting highlighted recently, because it has a clear relationship between relative error ratio and the computational complexity. However, the p-stable LSH computes hash values independent of the data distributions, and hence, sometimes the search fails or consumes considerably long time. For solving this problem, we propose Principal Component Hashing (PCH), which exploits the distribution of the stored data. Through experiments, we confirmed that PCH is faster than ANN and LSH at the same accuracy.

Keywords

Approximate Nearest Neighbor Search High dimensional space p-stable Locality Sensitive Hashing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yusuke Matsushita
    • 1
  • Toshikazu Wada
    • 1
  1. 1.Graduate School of Systems EngineeringWakayama UniversityWakayamaJapan

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