The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of variables that retain as much of the information in the ...

We also give a new formula to compute the total variance of modified principal components. As illustrations, SPCA is applied to real and simulated data with encouraging results. The purpose of the ...

The main advantage of using PCA for anomaly detection, compared to alternative techniques such as a neural autoencoder, is simplicity -- assuming you have a function that computes eigenvalues and ...

With this program the principal components of a system of data collected on eight crude oil samples which involved twenty-two variables were calculated. The first principal component accounted for 88 ...

Principal component analysis is often incorporated into genome-wide expression studies, but what is it and how can it be used to explore high-dimensional data?

Example 26.1: Principal Component Analysis The following example analyzes socioeconomic data provided by Harman (1976). The five variables represent total population, median school years, total ...

Principal Component Analysis (PCA) is widely used in data analysis and machine learning to reduce the dimensionality of a dataset. The goal is to find a set of linearly uncorrelated (orthogonal) ...