1004820.pdf
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 handlin...
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Taylor & Francis
2019
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oapen-20.500.12657-25274 |
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oapen-20.500.12657-252742023-02-01T09:35:32Z Spectral Feature Selection for Data Mining Zhao, Zheng Alan Liu, Huan Computer Science bic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining 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. 2019-04-11 23:55 2020-03-17 03:00:34 2020-04-01T10:32:49Z 2020-04-01T10:32:49Z 2012-01-01 book 1004820 OCN: 773311146 9781439862094 http://library.oapen.org/handle/20.500.12657/25274 eng application/pdf n/a 1004820.pdf Taylor & Francis 10.1201/b11426 102700 10.1201/b11426 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb b818ba9d-2dd9-4fd7-a364-7f305aef7ee9 9781439862094 Knowledge Unlatched (KU) 102700 KU Select 2018: STEM Backlist Books Knowledge Unlatched open access |
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English |
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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. |
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Taylor & Francis |
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2019 |
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