I'm trying to determine if I can plot my dissimilarity matrix (which is a Normalized Information Distance / the "Universal Distance Metric" dissimilarity matrix), in a lower-dimensional space using PCA or MDS (see Wikipedia article here if you are not familiar: https://en.wikipedia.org/wiki/Normalized_compression_distance).
I'm having a hard time determining if the Universal Distance metric / Normalized Information Distance is a "Euclidean" distance, which is required for PCA, and not necessary required for MDS (see this post: What's the difference between principal component analysis and multidimensional scaling?).
Also, the equivalent R functions to do a PCA reduction in R is prcomp(dissimilarityMatrix), and for mds is cmdscale(dissimilarityMatrix, eig=?, k=3). I assume if eig=TRUE, the MDS returns the same coordinates as prcomp, but if it's FALSE, then it differs?