Spectral Feature Selection for Data Mining
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series;
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34 870 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 18 April 2018
- ISBN 9781138112629
- Binding Paperback
- No. of pages224 pages
- Size 234x156 mm
- Weight 340 g
- Language English
- Illustrations 53 Illustrations, black & white; 18 Tables, black & white 0
Categories
Short description:
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.
MoreLong description:
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 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.
MoreTable of Contents:
Data of High Dimensionality and Challenges. Univariate Formulations for Spectral Feature Selection. Multivariate Formulations. Connections to Existing Algorithms. Large-Scale Spectral Feature Selection. Multi-Source Spectral Feature Selection. References. Index.
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