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 ...

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 ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on a classical ML technique that transforms a dataset into one with fewer columns, useful for creating a graph of ...

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.