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.

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 ...

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 61, No. 3 (1999), pp. 611-622 (12 pages) Principal component analysis (PCA) is a ubiquitous technique for data ...

Compositional data, consisting of vectors of proportions, have proved difficult to handle statistically because of the awkward constraint that the components of each vector must sum to unity. Moreover ...

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 ...