Multi Dimensional Scaling (MDS) - AAU-Dat/P5-Nonlinear-Dimensionality-Reduction GitHub Wiki
Principal Component Analysis (PCA) is a method of Multi-Dimensional Scaling (MDS). While PCA is a method, MDS is the whole family, or class, of methods, of which PCA is a part of. In certain use cases at least.
Complexities
The term MDS is actually not the correct one in literature that deals with this family of methods. Instead, one may use the Classical (Torgerson) MDS to refer to the method yielding similar results to PCA, and Non-Metric MDS for the non-linear variations.
It is used for, among other things, to create visualization data.
The distance matrix
MDS creates the distance matrix in a visually representable way.
Sources
https://www.youtube.com/watch?v=GH4QgpK9_Sc (REALLY good)
https://stackabuse.com/guide-to-multidimensional-scaling-in-python-with-scikit-learn/
https://scikit-learn.org/stable/auto_examples/manifold/plot_mds.html