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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 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.
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