dimensionality reduction

In Principal Component Analysis (PCA), we would like to convert our high-dimensional dataset onto a lower-dimensional space while keeping as much information as possible. Typically, this is done to avoid curse of dimensionality effects or for the purposes of data visualization. In broad strokes, PCA reduces the dimensionality of our dataset in a way that…