The papers in this special issue introduce the reader to the theory, algorithms, and
applications of principal component analysis (PCA) and its many extensions. The aim of …

Principal component analysis (PCA) is a statistical process that allows reducing number of
variables from a given dataset to a smaller set of variables that can be used in data analysis …

Principal component analysis (PCA) is a widely used technique for dimension reduction,
data processing, and feature extraction. The three tasks are particularly useful and important …

Principal components analysis (PCA) is a popular dimension reduction method and is
applied to analyze quantitative data. For PCA to qualitative data, nonlinear PCA can be …

Principal component analysis (PCA) is one of the most widely used unsupervised
dimensionality reduction methods in pattern recognition. It preserves the global covariance …

Principal Component Analysis (PCA) is one of the well-known linear dimensionality
reduction techniques in the literature. Large computational requirements of PCA and its …

The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics
technical and used orthogonal transformation to convert a set of observations of possibly …

This tutorial is designed to give the reader an understanding of Principal Components
Analysis (PCA). PCA is a useful statistical technique that has found application in fields such …

We propose a preprocessing method to improve the performance of Principal Component
Analysis (PCA) for classification problems composed of two steps; in the first step, the weight …

Nowadays, dealing with high dimension data has become common in many fields such as
network security, image processing, medical diagnosis, biometric systems, e-commerce, etc …