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 ...

Inside living cells, mitochondria divide, lysosomes travel, and synaptic vesicles pulse—all in three dimensions (3Ds) and constant motion. Capturing these events with clarity is vital not just for ...

Harvard University presents its eight-week online course through edX, which imparts to students essential knowledge of ...

In this article, we analyze approximate methods for undertaking a principal components analysis (PCA) on large datasets. PCA is a classical dimension reduction method that involves the projection of ...

Conventional dimension reduction methods deal mainly with simple data structure and are inappropriate for data with matrix-valued predictors. Li, Kim, and Altman (2010) proposed dimension folding ...