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Robust Methods for Data Reduction
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Robust Methods for Data Reduction

Robust Methods for Data Reduction

ByAlessio Farcomeni, Luca Greco
Edition 1st Edition
First Published 2015
eBook Published 13 January 2016
Pub. location New York
Imprint Chapman and Hall/CRC
DOIhttps://doi.org/10.1201/b18358
Pages 297 pages
eBook ISBN 9781466590632
SubjectsBioscience, Mathematics & Statistics
Get Citation

Get Citation

Farcomeni, A., Greco, L. (2015). Robust Methods for Data Reduction. New York: Chapman and Hall/CRC, https://doi.org/10.1201/b18358
ABOUT THIS BOOK

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

TABLE OF CONTENTS
chapter 1|28 pages
Introduction and Overview
View abstract
chapter 2|42 pages
Multivariate Estimation Methods
View abstract
part |2 pages
Part I Dimension Reduction
chapter |2 pages
Introduction to Dimension Reduction
View abstract
chapter 3|26 pages
Principal Component Analysis
View abstract
chapter 4|16 pages
Sparse Robust PCA
View abstract
chapter 5|16 pages
Canonical Correlation Analysis
View abstract
chapter 6|12 pages
Factor Analysis
View abstract
part |2 pages
Part II Sample Reduction
chapter |2 pages
Introduction to Sample Reduction
View abstract
chapter 7|22 pages
k-means and Model-Based Clustering
View abstract
chapter 8|18 pages
Robust Clustering
View abstract
chapter 9|20 pages
Robust Model-Based Clustering
View abstract
chapter 10|10 pages
Double Clustering
View abstract
chapter 11|12 pages
Discriminant Analysis
View abstract
chapter |14 pages
A Use of the Software R for Data Reduction
View 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

TABLE OF CONTENTS
chapter 1|28 pages
Introduction and Overview
View abstract
chapter 2|42 pages
Multivariate Estimation Methods
View abstract
part |2 pages
Part I Dimension Reduction
chapter |2 pages
Introduction to Dimension Reduction
View abstract
chapter 3|26 pages
Principal Component Analysis
View abstract
chapter 4|16 pages
Sparse Robust PCA
View abstract
chapter 5|16 pages
Canonical Correlation Analysis
View abstract
chapter 6|12 pages
Factor Analysis
View abstract
part |2 pages
Part II Sample Reduction
chapter |2 pages
Introduction to Sample Reduction
View abstract
chapter 7|22 pages
k-means and Model-Based Clustering
View abstract
chapter 8|18 pages
Robust Clustering
View abstract
chapter 9|20 pages
Robust Model-Based Clustering
View abstract
chapter 10|10 pages
Double Clustering
View abstract
chapter 11|12 pages
Discriminant Analysis
View abstract
chapter |14 pages
A Use of the Software R for Data Reduction
View abstract
CONTENTS
ABOUT THIS BOOK

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

TABLE OF CONTENTS
chapter 1|28 pages
Introduction and Overview
View abstract
chapter 2|42 pages
Multivariate Estimation Methods
View abstract
part |2 pages
Part I Dimension Reduction
chapter |2 pages
Introduction to Dimension Reduction
View abstract
chapter 3|26 pages
Principal Component Analysis
View abstract
chapter 4|16 pages
Sparse Robust PCA
View abstract
chapter 5|16 pages
Canonical Correlation Analysis
View abstract
chapter 6|12 pages
Factor Analysis
View abstract
part |2 pages
Part II Sample Reduction
chapter |2 pages
Introduction to Sample Reduction
View abstract
chapter 7|22 pages
k-means and Model-Based Clustering
View abstract
chapter 8|18 pages
Robust Clustering
View abstract
chapter 9|20 pages
Robust Model-Based Clustering
View abstract
chapter 10|10 pages
Double Clustering
View abstract
chapter 11|12 pages
Discriminant Analysis
View abstract
chapter |14 pages
A Use of the Software R for Data Reduction
View 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

TABLE OF CONTENTS
chapter 1|28 pages
Introduction and Overview
View abstract
chapter 2|42 pages
Multivariate Estimation Methods
View abstract
part |2 pages
Part I Dimension Reduction
chapter |2 pages
Introduction to Dimension Reduction
View abstract
chapter 3|26 pages
Principal Component Analysis
View abstract
chapter 4|16 pages
Sparse Robust PCA
View abstract
chapter 5|16 pages
Canonical Correlation Analysis
View abstract
chapter 6|12 pages
Factor Analysis
View abstract
part |2 pages
Part II Sample Reduction
chapter |2 pages
Introduction to Sample Reduction
View abstract
chapter 7|22 pages
k-means and Model-Based Clustering
View abstract
chapter 8|18 pages
Robust Clustering
View abstract
chapter 9|20 pages
Robust Model-Based Clustering
View abstract
chapter 10|10 pages
Double Clustering
View abstract
chapter 11|12 pages
Discriminant Analysis
View abstract
chapter |14 pages
A Use of the Software R for Data Reduction
View abstract
ABOUT THIS BOOK
ABOUT THIS BOOK

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

TABLE OF CONTENTS
chapter 1|28 pages
Introduction and Overview
View abstract
chapter 2|42 pages
Multivariate Estimation Methods
View abstract
part |2 pages
Part I Dimension Reduction
chapter |2 pages
Introduction to Dimension Reduction
View abstract
chapter 3|26 pages
Principal Component Analysis
View abstract
chapter 4|16 pages
Sparse Robust PCA
View abstract
chapter 5|16 pages
Canonical Correlation Analysis
View abstract
chapter 6|12 pages
Factor Analysis
View abstract
part |2 pages
Part II Sample Reduction
chapter |2 pages
Introduction to Sample Reduction
View abstract
chapter 7|22 pages
k-means and Model-Based Clustering
View abstract
chapter 8|18 pages
Robust Clustering
View abstract
chapter 9|20 pages
Robust Model-Based Clustering
View abstract
chapter 10|10 pages
Double Clustering
View abstract
chapter 11|12 pages
Discriminant Analysis
View abstract
chapter |14 pages
A Use of the Software R for Data Reduction
View 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

TABLE OF CONTENTS
chapter 1|28 pages
Introduction and Overview
View abstract
chapter 2|42 pages
Multivariate Estimation Methods
View abstract
part |2 pages
Part I Dimension Reduction
chapter |2 pages
Introduction to Dimension Reduction
View abstract
chapter 3|26 pages
Principal Component Analysis
View abstract
chapter 4|16 pages
Sparse Robust PCA
View abstract
chapter 5|16 pages
Canonical Correlation Analysis
View abstract
chapter 6|12 pages
Factor Analysis
View abstract
part |2 pages
Part II Sample Reduction
chapter |2 pages
Introduction to Sample Reduction
View abstract
chapter 7|22 pages
k-means and Model-Based Clustering
View abstract
chapter 8|18 pages
Robust Clustering
View abstract
chapter 9|20 pages
Robust Model-Based Clustering
View abstract
chapter 10|10 pages
Double Clustering
View abstract
chapter 11|12 pages
Discriminant Analysis
View abstract
chapter |14 pages
A Use of the Software R for Data Reduction
View abstract
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