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, and for some time, MMA does not have implementations that I can find of common methods [Sammon maps, local linear embedding (LLE), ISOmap to be specific]. Maybe I am looking in the wrong place (there is an opaque section in the machine learning that alludes to SOM for neural nets).
I have written my own implementations for metric and non-metric multidimensional scaling (found here with code for any fellow travelers), but would prefer not to invest the many hours to code these other methods. I’d really like to incorporate them into my MMA work flow, however.
I find this a bit frustrating, as MMA has lagged significantly behind R, Matlab, and Python in making these methods available. I have drunk the MMA coolade for some time now (love the aesthetics of the code, speed, ease of parallelization), but am having real trouble with the long lag in methods or community repositories in dimension reduction, graph theory (love Szabolcs iGraph implementation), and machine learning (better, but pretty idiosyncratic right now in MMA 11). And now I will stop my rant…
Does anybody know of MMA implementation of these methods [Sammon maps, local linear embedding (LLE), ISOmap]?
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