*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 ...*

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Language: en

Pages: 271

Pages: 271

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

Language: en

Pages: 13

Pages: 13

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

Language: en

Pages: 96

Pages: 96

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 --

Language: en

Pages: 425

Pages: 425

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 Much of the material is hitherto unpublished, thus

Language: en

Pages: 130

Pages: 130

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