Spectral Feature Selection for Data Mining (Open Access)

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 th Read more... 1. Data of high dimensionality and challenges -- 2. Univariate formulations for spectral feature selection -- 3. Multivariate formulations -- 4. Connections to existing algorithms -- 5. Large-scale spectral feature selection -- 6. Multi-source spectral feature selection. 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. Dedication......Page 6 Contents......Page 8 Preface......Page 12 Authors......Page 14 Symbol Description......Page 16 1. Data of High Dimensionality and Challenges......Page 18 2. Univariate Formulations for Spectral Feature Selection......Page 38 3. Multivariate Formulations......Page 72 4. Connections to Existing Algorithms......Page 100 5. Large-Scale Spectral Feature Selection......Page 126 6. Multi-Source Spectral Feature Selection......Page 160 References......Page 188 Zheng Alan Zhao, Huan Liu. A Champman & Hall Book--cover Page. Includes Bibliographical References.