Dimensionality Reduction

Dimensionality Reduction

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Description

Dimensionality reduction (DR) refers to the problem of projecting high-dimensional data onto a low-dimensional manifold so that relevant information is preserved. DR arises in many application areas where direct processing of the data is too costly. Through a machine-learning perspective that focuses on algorithms rather than theory, "Dimensionality Reduction" provides an overview of methods for DR including real-world applications taken from areas such as speech processing and computer vision. Interest in this area has exploded in recent years, making it a growing field of research. This book serves as the first reference for interested graduate students and researchers.show more

Product details

  • Hardback | 320 pages
  • 156 x 235mm
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • United States
  • English
  • 30 black & white illustrations
  • 1584886536
  • 9781584886532

Table of contents

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About Miguel A. Carreira-perpinan

Oregon Health & Science University, Beauerton, USA Rutgers University, New Brunswick, New Jersey, USA Carnegie Mellon University, Pennsylvania, USA Royal Holloway University of London, UK University of California, Irvine, California, USAshow more