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Universal Features for High-Dimensional Learning and Inference

Shao-Lun Huang, Anuran Makur, Gregory W. Wornell & Lizhong Zheng

Universal Features for High-Dimensional Learning and Inference
Universal Features for High-Dimensional Learning and Inference

Universal Features for High-Dimensional Learning and Inference

Shao-Lun Huang, Anuran Makur, Gregory W. Wornell & Lizhong Zheng

Paperback | English
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Description

In this monograph, the authors develop the idea of extracting “universally good” features of data, and establish that diverse notions of such universality lead to precisely the same features. The information-theoretic approach used results in a local information geometric analysis that facilitates their computation in a host of applications.

Specifications

  • Publisher
    now publishers Inc
  • Pub date
    Feb 2024
  • Pages
    320
  • Theme
    Information architecture
  • Dimensions
    234 x 156 mm
  • Weight
    435 gram
  • EAN
    9781638281764
  • Paperback
    Paperback
  • Language
    English