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

By: Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC data mining and knowledge discovery seriesPublisher: Boca Raton, FL : CRC Press, ©2012Description: 1 online resource (xv, 195 pages) : illustrations (some color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781439862100
  • 1439862109
  • 9781439862094
  • 1439862095
Subject(s): Genre/Form: Additional physical formats: Print version:: Spectral feature selection for data mining.DDC classification:
  • 006.3/12 23
Online resources:
Contents:
1. Data of high dimensionality and challenges -- 2. Univariate formulations for spectral feature selection -- 3. Multivariate formulations -- 4. Connections to existing algorithms -- 5. Large-scale spectral feature selection -- 6. Multi-source spectral feature selection.
Summary: 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 th.
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Includes bibliographical references and index.

Print version record.

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 th.

1. Data of high dimensionality and challenges -- 2. Univariate formulations for spectral feature selection -- 3. Multivariate formulations -- 4. Connections to existing algorithms -- 5. Large-scale spectral feature selection -- 6. Multi-source spectral feature selection.

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