I am working on a visualization manuscript that uses dimensional reduction with tSNE of a very large data set (227,573 points x 2,571 features). The reviewers would like figures comparing the tSNE projections with other common methods of dimension reduction, aside from PCA. Unfortunately, MMA does not have implementations that I can find of specific dimension reduction methods commonly compared and cited in the field [Sammon maps, local linear embedding (LLE), ISOmap to be specific], and readily available in R, Matlab, and Python.
I have written implementations for metric and non-metric multidimensional scaling (found here with code for any fellow travelers), but am hoping somebody has developed these in MMA to save hours of coding.
Does anybody know of MMA implementations/code for these methods [Sammon maps, local linear embedding (LLE), ISOmap]?
That said, I am a bit frustrated with the long lag in MMA implementation of methods in dimension reduction, graph theory (love Szabolcs iGraph implementation), and machine learning (better, but idiosyncratic in MMA 11).