Nature

Sparse Principal Component Analysis in High-Dimensional Data

Sparse Principal Component Analysis (sparse PCA) represents a significant advance in the field of dimensionality reduction for high-dimensional data. Unlike conventional Principal Component Analysis ...
CU Boulder News & Events

Understanding Principal Component Analysis in PyTorch

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 ...
Visual Studio Magazine

Principal Component Analysis (PCA) from Scratch Using the Classical Technique with C#

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.
Visual Studio Magazine

Principal Component Analysis from Scratch Using Singular Value Decomposition with C#

Principal Component Analysis from Scratch Using Singular Value Decomposition with C# Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on a classical ML technique ...
JSTOR Daily

Generalized additive models with principal component analysis: an application to time series of respiratory disease and air pollution data

Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 67, No. 2 (FEBRUARY 2018), pp. 453-480 (28 pages) Environmental epidemiological studies of the health effects of air ...