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 ...

This preliminary study aims to identify soft tissue facial morphological features that vary among ethnically diverse, facially balanced adults using Principal Component Analysis (PCA) of 3D facial ...

Principal components analysis (PCA) is frequently used in biological sciences for global analysis of omics datasets. It provides fully unsupervised information on the dominant directions of highest ...