Methods that are utilized to reduce the dimensionality of a dataset. - [[Principal component analysis | Principal component analysis (PCA)]] : attempts to preserve as much of the data variance as we we reduce dimensionality - [[Multi-dimensional scaling | Multi-dimensional scaling (MDS)]]: attempts to preserve the relative distances between points as we reduce dimensionality - [[Factor analysis]]: Similar to MDS and PCA, but its original motivation was different. The idea of factor analysis is to extract “factors” - a smaller number of “hidden” variables that are responsible for the larger set of observed (recorded, measured) variables.