Modern Multivariate Statistical Techniques : Regression, Classification, and Manifold Learning
Publication year: 2008
ISBN: 978-0-387-78189-1
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Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods.
Subject: Mathematics and Statistics, Boosting, Clustering, Factor analysis, Latent variable model, Linear discriminant analysis, Mathematica, Projection pursuit, Random Forest, Support Vector Machine, bootstrap aggregating, cluster analysis, data analysis, data mining, machine learning, multidimensional scaling
