A good way to see where this article is headed is to take a look at the screen shot of a demo program shown in Figure 1. The demo sets up a dummy dataset of six items: [ 5.1 3.5 1.4 0.2] [ 5.4 3.9 1.7 ...

Ethnicity can confound results in pharmacogenomic studies. Allele frequencies of loci that influence drug metabolism can vary substantially between different ethnicities and underlying ancestral ...

PCA is an important tool for dimensionality reduction in data science and to compute grasp poses for robotic manipulation from point cloud data. PCA can also directly used within a larger machine ...

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, ...

A very important technique in unsupervised machine learning as well as dimensionality reduction is Principal Component Analysis (PCA). But PCA is difficult to understand without the fundamental ...

Principal components analysis (PCA) is a common dimension-reduction tool that transforms a set of variables into a linearly uncorrelated set of variables. Standard PCA estimators involve either the ...