# Principal component analysis

#### http://schema.org/description

• "Principal component analysis is central to the study of multivariate data. This book includes core material, research and a wide range of applications. It is suitable for researchers in statistics and for those who use principal component analysis. It requires some knowledge of matrix algebra."
• "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."@en
• ""Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account on the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra"--Back cover."@en
• "Properties of population principal components. Properties of sample principal e components. Interpreting principal components: examples. Graphical representation of data using principal components. Choosing a subset of principal components of variables. Principal component analysis and factor analysis. Principal components in regression analysis. principal components used with other multivariate techniques. Outlier detection, influential observations and robust estimation. Rotation and interpretation odd principal components. PCA for time series and other non-independent data. Principal component analysis for special types of data. Generalizations and adaptations of principal component analysis. A computation of principal components."
• "This second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. (Midwest)."
• "Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques, it continues to be the subject of much research, ranging from new model-based approaches to algorithmic ideas from neural networks. It is extremely versatile, with applications in many disciplines. The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition. Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra. Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books. His research interests are broad, but aspects of principal component analysis have fascinated him and kept him busy for over 30 years."@en
• "Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques, it continues to be the subject of much research, ranging from new model-based approaches to algorithmic ideas from neural networks. It is extremely versatile, with applications in many disciplines. The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition. Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra. Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books."

#### http://schema.org/genre

• "Llibres electrònics"
• "Livre électronique (Descripteur de forme)"
• "Ressource Internet (Descripteur de forme)"
• "Electronic books"@en
• "Electronic books"
• "Livres électroniques"
• "Handboeken (vorm)"

#### http://schema.org/name

• "Principal component analyses"
• "Principal Component Analysis"
• "Principal component analysis"
• "Principal component analysis"@en