Principal Component Analysis

Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the ...

Principal Component Analysis

Principal Component Analysis

Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.

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Principal Component Analysis
Language: en
Pages: 271
Authors: I.T. Jolliffe
Categories: Mathematics
Type: BOOK - Published: 2013-03-09 - Publisher: Springer Science & Business Media

Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in
Application of Principal Component Analysis (PCA) and Improved Joint Probability Distributions to the Inverse First-order Reliability Method (I-FORM) for Predicting Extreme Sea States
Language: en
Pages: 13
Authors: I.T. Jolliffe
Categories: Mathematics
Type: BOOK - Published: 2016 - Publisher:

Environmental contours describing extreme sea states are generated as the input for numerical or physical model simulations as a part of the standard current practice for designing marine structures to survive extreme sea states. These environmental contours are characterized by combinations of significant wave height (Hs) and either energy period
Principal Components Analysis
Language: en
Pages: 96
Authors: George H. Dunteman
Categories: Mathematics
Type: BOOK - Published: 1989-05 - Publisher: SAGE

For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra --
Principal Component Analysis in Meteorology and Oceanography
Language: en
Pages: 425
Authors: Rudolph W. Preisendorfer
Categories: Science
Type: BOOK - Published: 1988 - Publisher: Elsevier Science Limited

This book, the only one of its kind available, presents PCA from its simplest form through its abstract formalism, including applications. Furthermore, it extends the use of PCA far beyond its well-known applications to scalar (e.g. temperature) or vector (e.g. wind) fields. Much of the material is hitherto unpublished, thus
Principal Component Analysis
Language: en
Pages: 130
Authors: Virginia Gray
Categories: Science
Type: BOOK - Published: 2017-05-01 - Publisher:

This book provides new research on principal component analysis (PCA). Chapter One introduces typical PCA applications of transcriptomic, proteomic and metabolomic data. Chapter Two studies the factor analysis of an outcome measurement survey for science, technology and society. Chapter Three examines the application of PCA to performance enhancement of hyperspectral

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