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.

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. 36, No. 6, High Dimensional Inference and Random Matrices (Dec., 2008), pp. 2791-2817 (27 pages) Principal component analysis (PCA) is a standard tool for dimensional ...

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 ...