Similarity Search in High-Dimensional Vector Spaces

Similarity Search in High-Dimensional Vector Spaces

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This dissertation addresses the problem of identifying the most similar objects in a database given a set of reference objects and a set of features. It investigates the so-called "Curse of Dimensionality", and presents an organization for NN-Search ("Nearest Neighbour Search") optimized for high-dimensional spaces - the so-called "Vector Approximation File" (VA-File). The text shows the superiority of the VA-File theoretically and through experiments. The VA-File is also discussed with reference to approximate search and parallel search in a cluster of workstations. This dissertaion also provides an indexing technique that allows for interactive-time similarity search even in huge databases.
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Product details

  • Paperback | 240 pages
  • 147.3 x 205.7 x 15.2mm | 340.2g
  • IOS Press,US
  • Amsterdam, United States
  • English
  • 1586031775
  • 9781586031770