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    Spectral Feature Selection for Data Mining

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    Author(s)
    Zhao, Zheng Alan
    Liu, Huan
    Collection
    Knowledge Unlatched (KU)
    Number
    102700
    Language
    English
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    Abstract
    This timely introduction to spectral feature selection illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. It presents the theoretical foundations of spectral feature selection, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. 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. Source code for the algorithms is available online.
    URI
    http://library.oapen.org/handle/20.500.12657/25274
    Keywords
    Computer Science
    DOI
    10.1201/b11426
    ISBN
    9781439862094
    OCN
    773311146
    Publisher
    Taylor & Francis
    Publisher website
    https://taylorandfrancis.com/
    Publication date and place
    2012-01-01
    Grantor
    • Knowledge Unlatched - 102700 - KU Select 2018: STEM Backlist Books
    Classification
    Data mining
    Rights
    https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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