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0 of 2 people found the following review helpful
on April 1, 2012
Format: Hardcover
Table of Contents

Chapter 1. Data of High Dimensionality and Challenges
Dimensionality Reduction Techniques
Feature Selection for Data Mining
Spectral Feature Selection
Organization of the Book

Chapter 2. 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
Robustness Analysis for SPEC
Discussions

Chapter 3. Multivariate Formulations
The Similarity Preserving Nature of SPEC
A Sparse Multi-Output Regression Formulation
Solving the L2,1-Regularized Regression Problem
Efficient Multivariate Spectral Feature Selection
A Formulation Based on Matrix Comparison
Feature Selection with Proposed Formulations

Chapter 4. Connections to Existing Algorithms
Connections to Existing Feature Selection Algorithms
Connections to Other Learning Models
An Experimental Study of the Algorithms
Discussions

Chapter 5. 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
Parallel MRSF
Parallel MCSF
Discussions

Chapter 6. Multi-Source Spectral Feature Selection
Categorization of Different Types of Knowledge
A Framework Based on Combining Similarity Matrices
A Framework Based on Rank Aggregation
Experimental Results
Discussions

References

Index
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