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.
Spectral feature selection is a state-of-the-art technique based on spectral graph theory. This book provides a comprehensive introduction to spectra, including its theoretical foundations, connections to existing feature selection and extraction algorithms, and its applications in solving novel real-world problems in feature selection. It covers general feature selection and feature extraction concepts, the most popular existing algorithms, and recent research developments. The text also includes precise definitions, a number of illustrative figures, and plenty of examples, along with slides, source code, and data on a supporting website.
