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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...

Materie / parole chiave: Principal component analysis; Dimensionality reduction; Representation (politics); Curse of dimensionality; Process (computing); Computer science; Reduction (mathematics); Component (thermodynamics); Data mining; Mathema...

Materie / parole chiave: Principal component analysis; Component (thermodynamics); Computer science; Physics; Artificial intelligence; Thermodynamics

Materie / parole chiave: 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...

This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some s...

Materie / parole chiave: Psychology; Statistical analysis; Principal component analysis; Principal (computer security); Social psychology; Cognitive psychology; Mathematics education; Statistics; Computer science; Mathematics; Operating system

Economic inequality is a social phenomenon with several facets, all of which are signifificant when it comes to multidimensional measurement. When income is used as the fundamental variable of analysis to measure economi...

A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spa...

The classic, multivariate technique of principal component analysis can be used to find and estimate the directions of lines and planes of best least-squares fit along the demagnetization path of a palaeomagnetic specime...

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...

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...

(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...

I. INTRODUCTION. 1. Experimental Design. II. SINGLE FACTOR EXPERIMENTS. 2. Sources of Variability and Sums of Squares. 3. Variance Estimates and F Ratio. 4. Analytical Comparisons Among Means. 5. Analysis of Trend. 6. Si...

Cross-sectional data from the Medical Outcomes Study (MOS) were analyzed to test the validity of the MOS 36-Item Short-Form Health Survey (SF-36) scales as measures of physical and mental health constructs. Results from ...

Materie / parole chiave: Independent component analysis; Principal component analysis; Transformation (genetics); Pattern recognition (psychology); Algorithm; Mathematics; Computer science; Artificial intelligence; Biochemistry; Gene; Chemistry

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

A new multivariate analysis technique, developed to relate community composition to known variation in the environment, is described.The technique is an extension of correspondence analysis (reciprocal averaging), a popu...

Materie / parole chiave: Ordination; Biplot; Principal component analysis; Correspondence analysis; Detrended correspondence analysis; Euclidean distance; Biology; Multivariate statistics; Canonical correspondence analysis; Univariate; Ecology; ...