Short Introduction to PCA

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 minimizes (certain aspects of) the amount of information we throw away by projecting our p-dimensional feature set onto a lower-dimensional subspace. Continue reading “Short Introduction to PCA”