Principal component analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information.

PCA is a concept that is traditionally hard to grasp so instead of giving you the n’th mathematical derivation I will provide you with some intuition.

Basically PCA is nothing else but a projection of some higher dimensional object into a lower dimension. What sounds complicated is really something we encounter every day: when we watch TV we see a 2D-projection of 3D-objects!

Click through for the rest of the story.

Kevin Feasel

2018-12-07

Data Science, R