Robust Methods for Data Reduction (Hardcover)

Introduction and Overview
What is contamination
Evaluating robustness
What is data reduction
An overview of robust dimension reduction
An overview of robust sample reduction
Example datasets

Multivariate Estimation Methods
Robust univariate methods
Classical multivariate estimation
Robust multivariate estimation
Identification of multivariate outliers
Examples

Dimension Reduction
Principal Component Analysis
Classical PCA
PCA based on robust covariance estimation
PCA based on projection pursuit
Spherical PCA
PCA in high dimensions
Outlier identification using principal components
Examples

Sparse Robust PCA
Basic concepts and sPCA
Robust sPCA
Choice of the degree of sparsity
Sparse projection pursuit
Examples

Canonical Correlation Analysis
Classical canonical correlation analysis
CCA based on robust covariance estimation
Other methods
Examples

Factor Analysis
The FA model
Robust factor analysis
Examples

Sample Reduction
k-Means and Model-Based Clustering
A brief overview of applications of cluster analysis
Basic concepts
k-means
Model-based clustering
Choosing the number of clusters

Robust Clustering
Partitioning around medoids
Trimmed k-means
Snipped k-means
Choosing the trimming and snipping levels
Examples

Robust Model-Based Clustering
Robust heterogeneous clustering based on trimming
Robust heterogeneous clustering based on snipping
Examples

Double Clustering
Double k-means
Trimmed double k-means
Snipped double k-means
Robustness properties

Discriminant Analysis
Classical discriminant analysis
Robust discriminant analysis