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, double clustering, and discriminant
analysis. The first part of the book illustrates how dimension
reduction techniques synthesize available information by reducing
the dimensionality of the data. The second part focuses on cluster
and discriminant analysis. The authors explain how to perform
sample reduction by finding groups in the data. Despite
considerable theoretical achievements, robust methods are not often
used in practice. This book fills the gap between theoretical
robust techniques and the analysis of real data sets in the area of
data reduction. Using real examples, the authors show how to
implement the procedures in R. The code and data for the examples
are available on the book's CRC Press web page.
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