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Principal Component Analysis

Principal Component Analysis is een boek van Nova Science Publishers;

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Principal Component Analysis

analysis in data analysis. Principal component analysis involves a statistical procedure which orthogonally transforms a set of possibly correlated;

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Principal Component Analysis

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Principal Component Analysis

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Principal Component Analysis

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Diverse Applications of Principal Component Analysis

This book emphasizes on the diverse applications of principal component analysis. The aim of this book is to enhance knowledge;

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Principal Component Analysis

The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates;

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Principal Component Analysis

The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates;

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Principal Component Analysis Handbook

This book on Principal component analysis (PCA) is a significant contribution to the field of data analysis. PCA involves a statistical;

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Principal Component Neural Networks

Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination;

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Face Recognition

control systems. Topics discussed in this compilation include two-dimensional principal component analysis algorithms for face recognition;

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Sparse representation of High Dimensional Data for Classification

In this book you will find the use of sparse Principal Component Analysis (PCA) for representing high dimensional data for classification;

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Constrained Principal Component Analysis and Related Techniques

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA;

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Constrained Principal Component Analysis and Related Techniques

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA;

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Applied Multivariate Statistical Analysis - Summaries of theory and Exercises solved

summaries, and exercises solved, on the following topics: Multiple Linear Regression, Principal Component Analysis (without and with Varimax;

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A User's Guide to Principal Components

practitioners of principal component analysis. Among the topics explored are extension to p variables, scaling input data, inferential procedures;

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Applied Factor Analysis in the Natural Sciences

in this book, such as classical principal components, principal component factor analysis, principal coordinate analysis, and correspondence;

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Multivariate Archaeology

statistics, he describes three multivariate deductive methods (principal component, principal coordinate and correspondence analyses);

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Statistical Analysis of Financial Data in S-plus

component analysis of yield curves. The book is aimed at undergraduate students in financial engineering; master students in finance and MBA's, and;

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Independent Component Analysis

decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers;

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Robust Methods for Data Reduction

important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component;

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Robust Methods for Data Reduction

important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component;

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Advances in Chromatography

Volume 36 examines timely subjects such as multilinear regression, canonical correlation, and facor and principal component methods;

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Computational Methods With Applications in Bioinformatics Analysis

, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations;

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Fundamentals of Data Analytics

and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral;

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Fundamentals of Data Analytics

and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral;

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PCA Demonstrates Trunk Muscle Pattern Variation with Fall Direction

curves for the four different fall directions displayed different characteristics. Observations were supported by principal component analysis;

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