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, ...

The Annals of Statistics, Vol. 46, No. 5 (October 2018), pp. 2416-2451 (36 pages) A central problem of random matrix theory is to understand the eigenvalues of spiked random matrix models, introduced ...

Transforming a dataset into one with fewer columns is more complicated than it might seem, explains Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step machine learning tutorial.