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.

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 ...

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 ...

Several measurement techniques used in the life sciences gather data for many more variables per sample than the typical number of samples assayed. For instance, DNA microarrays and mass spectrometers ...

In this article, we analyze approximate methods for undertaking a principal components analysis (PCA) on large datasets. PCA is a classical dimension reduction method that involves the projection of ...

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, ...