Principal component analysis (PCA) simplifies the complexity in high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions, which act as ...

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 ...

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 ...

A principal component analysis of a covariance matrix gives more weight to variables with larger variances. A principal component analysis of a covariance matrix is equivalent to an analysis of a ...

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

This article will cover the theory underpinning multivariate analysis of variance (MANOVA), which expands on the capabilities of ANOVA, the types of MANOVA and a worked example of the test.