Spectral Feature Selection for Data Mining (Paperback)

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.

General

Imprint:	Crc Press
Country of origin:	United Kingdom
Series:	Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Release date:	April 2018
First published:	2012
Authors:	Zheng Alan Zhao • Huan Liu
Dimensions:	234 x 156mm (L x W)
Format:	Paperback
Pages:	224
ISBN-13:	978-1-138-11262-9
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	1-138-11262-3
Barcode:	9781138112629

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