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 ...

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.

The Annals of Statistics, Vol. 43, No. 3 (June 2015), pp. 1300-1322 (23 pages) Estimating the leading principal components of data, assuming they are sparse, is a central task in modern ...

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, ...

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.

Correspondence: Dr J Li, Institute of Animal Sciences, Chinese Academy of Agricultural Science, Beijing, People’s Republic of China. E-mail: [email protected] and Dr R Yang, Research Centre for ...