Spectral feature selection for data mining

Zheng Alan Zhao, Huan Liu

Research output: Book/ReportBook

30 Citations (Scopus)

Abstract

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 selections. 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.

Original languageEnglish (US)
PublisherCRC Press
Number of pages199
ISBN (Electronic)9781439862100
ISBN (Print)9781138112629
StatePublished - Jan 1 2011

Fingerprint

Data mining
Feature extraction
Feature selection

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)
  • Computer Science(all)

Cite this

Spectral feature selection for data mining. / Zhao, Zheng Alan; Liu, Huan.

CRC Press, 2011. 199 p.

Research output: Book/ReportBook

Zhao ZA, Liu H. Spectral feature selection for data mining. CRC Press, 2011. 199 p.
Zhao, Zheng Alan ; Liu, Huan. / Spectral feature selection for data mining. CRC Press, 2011. 199 p.
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