ABSTRACT

Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, dou

chapter 1|28 pages

Introduction and Overview

chapter 2|42 pages

Multivariate Estimation Methods

part |2 pages

Part I Dimension Reduction

chapter |2 pages

Introduction to Dimension Reduction

chapter 3|26 pages

Principal Component Analysis

chapter 4|16 pages

Sparse Robust PCA

chapter 5|16 pages

Canonical Correlation Analysis

chapter 6|12 pages

Factor Analysis

part |2 pages

Part II Sample Reduction

chapter |2 pages

Introduction to Sample Reduction

chapter 7|22 pages

k-means and Model-Based Clustering

chapter 8|18 pages

Robust Clustering

chapter 9|20 pages

Robust Model-Based Clustering

chapter 10|10 pages

Double Clustering

chapter 11|12 pages

Discriminant Analysis