I am comparing dimension reduction techniques and I am utilizing them for data visualizations onto a plane – projections in 2D space.
The input into a projection/dimension reduction techniques is a symmetric similarity matrix which is constructed from many items. The goal of the projection is in the task of visualizing similar items close to each other.
I have chosen and described already two techniques – PCA and t-SNE. They are clearly different and have some aspects. For example, the resulting visualizations of t-SNE are better than PCA since t-SNE preserves small pairwise distances and my goal is in the finding the similarities of items, not about preserving the large distances and maximizing variance as PCA does. Just as PCA is a wrong choice in this, I would like to find another approach maybe distinct from PCA, t-SNE.
But what could be another technique to compare them to? Multidimensional scaling?
I would like it to be another well-performing and often used technique. There are publications which describe many (FODOR, Imola K. A survey of dimension reduction techniques. Lawrence Livermore National Lab., CA (US), 2002...) but I would like to get a fast advice which technique's performance could be great to compare with PCA and t-SNE.
Big thanks to you for a response!