Description

Similarity search in high-dimensional spaces is popular for applications like image

processing, time series, and genome data. In higher dimensions, the phenomenon of

curse of dimensionality kills the effectiveness of most of

Similarity search in high-dimensional spaces is popular for applications like image

processing, time series, and genome data. In higher dimensions, the phenomenon of

curse of dimensionality kills the effectiveness of most of the index structures, giving

way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity

searches. In addition to range searches and k-nearest neighbor searches, there

is a need to answer negative queries formed by excluded regions, in high-dimensional

Reuse Permissions
  • 2.01 MB application/pdf

    Download count: 0

    Details

    Contributors
    Date Created
    • 2016
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: M.S., Arizona State University, 2016
      Note type
      thesis
    • Includes bibliographical references (pages 71-75)
      Note type
      bibliography
    • Field of study: Computer science

    Citation and reuse

    Statement of Responsibility

    by Aneesha Bhat

    Machine-readable links