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            Spectral Feature Selection for Data Mining

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            Auteur
            Zhao, Zheng Alan
            Liu, Huan
            Collection
            Knowledge Unlatched (KU)
            Language
            English
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            Résumé
            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
            https://doab-dev.siscern.org/handle/20.500.12854/185392
            Keywords
            Computer Science; thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
            DOI
            10.1201/b11426
            ISBN
            9781439862094
            Publisher
            Taylor & Francis
            Publisher website
            http://www.taylorandfrancis.com/
            Publication date and place
            2012-01-01
            Grantor
            • Knowledge Unlatched
            • OAPEN harvesting collection

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            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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