Since the publication of the bestselling first edition, many advances have been made in exploratory data analysis (EDA). Covering innovative approaches for dimensionality reduction, clustering, and visualization, Exploratory Data Analysis with MATLABAr, Second Edition uses numerous examples and applications to show how the methods are used in practice. New to the Second Edition Discussions of nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, and smoothing splines An expanded set of methods for estimating the intrinsic dimensionality of a data set Several clustering methods, including probabilistic latent semantic analysis and spectral-based clustering Additional visualization methods, such as a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a new method called Andrewsa images Instructions on a free MATLAB GUI toolbox for EDA Like its predecessor, this edition continues to focus on using EDA methods, rather than theoretical aspects. The MATLAB codes for the examples, EDA toolboxes, data sets, and color versions of all figures are available for download at http://pi-sigma.infoi Thus, we can project the data using this MATLAB code: % Now project the data onto the theta-line. ... Ic 0= a 2.2 Principal Component Analysis a PCA The main purpose of principal component analysis (PCA) is to reduce the dimensionalityanbsp;...
|Title||:||Exploratory Data Analysis with MATLAB, Second Edition|
|Author||:||Wendy L. Martinez, Angel Martinez, Jeffrey Solka|
|Publisher||:||CRC Press - 2010-12-16|