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Introduction * Properties of Population Principal Components * Properties of Sample Principal Components * Interpreting Principal Components: Examples * Graphical Representation of Data Using Principal Components * Choos...

Abstract Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter‐correlated quantitative dependent variables. Its goal is to extrac...

Materias / palabras clave: Principal component analysis; Outlier; Matrix (chemical analysis); Multidimensional scaling; Data Matrix; Scaling; Disjoint sets; Set (abstract data type); Variance (accounting); Variable (mathematics); Statistics; Data ...

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same...

Materias / palabras clave: Psychology; Statistical analysis; Principal component analysis; Principal (computer security); Social psychology; Cognitive psychology; Mathematics education; Statistics; Computer science; Mathematics; Operating system

Materias / palabras clave: Population stratification; Spurious relationship; Biology; Principal component analysis; Allele frequency; Genetic association; Genome-wide association study; Population; Evolutionary biology; Computational biology; Geno...

Materias / palabras clave: Independent component analysis; Principal component analysis; Representation (politics); Computer science; Algorithm; Gaussian; Artificial intelligence; Projection (relational algebra); Pattern recognition (psychology); ...

Preface to the Third Edition.Preface to the Second Edition.Preface to the First Edition.1. Introduction.2. The Multivariate Normal Distribution.3. Estimation of the Mean Vector and the Covariance Matrix.4. The Distributi...

The objective of this work was to develop a multivariate strategy to optimize tablet manufacturing employing mephenesin as model drug. The process variables for granulation step were binders and lubricants types, while ...

Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningfu...

This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or N-way array. Decompositions of higher-order tensors (i.e., N-way array...

High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuni...

(NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED. 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. D...

We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here f...