Author(s) Name:  Zheng Alan Zhao, Huan Liu

About the Book:

   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.

Table of Contents

Data of High Dimensionality and Challenges

  Data of High Dimensionality and Challenges Dimensionality Reduction Techniques
  Feature Selection for Data Mining
  Spectral Feature Selection
  Organization of the Book

Univariate Formulations for Spectral Feature Selection

  Modeling Target Concept via Similarity Matrix
  The Laplacian Matrix of a Graph
  Evaluating Features on the Graph
  An Extension for Feature Ranking Functions
  Spectral Feature Selection via Ranking

Multivariate Formulations

  The Similarity Preserving Nature of SPEC
  A Sparse Multi-Output Regression Formulation
  Efficient Multivariate Spectral Feature Selection
  A Formulation Based on Matrix Comparison
  Feature Selection with Proposed Formulations

Connections to Existing Algorithms

  Connections to Existing Feature Selection Algorithms
  Connections to Other Learning Models
  An Experimental Study of the Algorithms

Large-Scale Spectral Feature Selection

  Large-Scale Spectral Feature Selection Data Partitioning for Parallel Processing
  MPI for Distributed Parallel Computing
  Parallel Spectral Feature Selection
  Computing the Similarity Matrix in Parallel
  Parallelization of the Univariate Formulations

Multi-Source Spectral Feature Selection

  Categorization of Different Types of Knowledge
  A Framework Based on Combining Similarity Matrices
  A Framework Based on Rank Aggregation

ISBN:  9781439862094

Publisher:  Chapman and Hall/CRC

Year of Publication:  2011

Book Link:  Home Page Url