Spectral Feature Selection for Data Mining

Zheng Alan Zhao, Huan Liu

January 31, 2018 Forthcoming by Chapman and Hall/CRC
Reference - 219 Pages - 53 B/W Illustrations
ISBN 9781138112629 - CAT# K35192
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series


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  • Presents the principles of spectral feature selection, a new technique that addresses the challenges of high-dimensional data
  • Describes new techniques for high-performance parallel feature selection and multisource feature selection
  • Covers existing dimensionality reduction methods
  • Requires only some basic knowledge of linear algebra, probability theory, and convex optimization
  • Includes concrete examples and figures in each chapter
  • Offers the source code for the algorithms on a supplementary website


Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervised feature selection.

The book explores the latest research achievements, sheds light on new research directions, and stimulates readers to make the next creative breakthroughs. It presents the intrinsic ideas behind spectral feature selection, its theoretical foundations, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. The authors also cover feature selection and feature extraction, including basic concepts, popular existing algorithms, and applications.

A timely introduction to spectral feature selection, this book illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection.


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