There are two main techniques to implement PCA. The first technique, sometimes called classical, computes eigenvalues and eigenvectors from a covariance matrix derived from the source data. The second ...

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 ...

We examined the ability of eigenvalue tests to distinguish field-collected from random, assemblage structure data sets. Eight published time series of species abundances were used in the analysis, ...