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 ...

The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of variables that retain as much of the information in the ...

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 ...

We introduce a novel method of principal component analysis for data with varying domain lengths for each functional observation. We refer to this technique as variable-domain functional principal ...

Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), Vol. 26, No. 4 (Dec., 1964), pp. 329-358 (30 pages) The paper provides various interpretations of principal components in the analysis ...

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 ...