Principal Component Analysis (PCA) is a powerful statistical technique for variable
reduction, It used when variables are highly correlated. PCA becomes an essential tool for …

This book is aimed at raising awareness of researchers, scientists and engineers on the
benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will …

Abstract Principal Component Analysis (PCA) is a well-known linear dimensionality
reduction technique in the literature. It extracts global principal components (PCs) and lacks …

Big databases are increasingly widespread and are therefore hard to understand, in
exploratory biomedicine science, big data in health research is highly exciting because data …

Abstract Principal Components Analysis (PCA) is a practical and standard statistical tool in
modern data analysis that has found application in different areas such as face recognition …

Dimensionality reduction is one of the preprocessing steps in many machine learning
applications and it is used to transform the features into a lower dimension space. Principal …

Principal component analysis (PCA) is a statistical methodology that uses orthogonal
transformation to convert a set of observations of possibly correlated variables into a set of …

Dimensionality reduction is one of the preprocessing steps in many machine learning
applications and it is used to transform the features into a lower dimension space. Principal …

Principal component analysis (PCA) is a mathematical procedure which uses sophisticated
mathematical principles to transform a number of correlated variables into a smaller number …

Abstract Principal Component Analysis (PCA) is a useful technique for reducing the
dimensionality of datasets for compression or recognition purposes. Many different methods …