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I tried applying tsne on an neuroimaging dataset analysis for which I had been using principal coordinate ordination techniques. Results were just OK. The imple

I tried applying tsne on an neuroimaging dataset analysis for which I had been using principal coordinate ordination techniques. Results were just OK. The implementation I used in R had a number of parameters to tune, but my interpretation of the visualization changed with those parameters...so I was unsure which to go with. I wonder if one reason for my difficulty was the noise inherent in functional MRI data.

lmeyerov 8y ago
I found that true of t-sne (and umap + tda) in general. Huge sensitivity to feature encoding and parameter tuning, and libraries in practice fail to help there, which seems a barrier for the huge pool of potential users. Most of all, I've struggled with categorical dimensions (people, computers, things, and all sorts of things you see in most non-scientific settings). That comes back again to the encoding problem. Would be genuinely interested to solutions there. I play every 3-6mo with these for finding something usable to add to Graphistry. While library devs talk about efforts here, seems to be an on-going challenge. In a sense, Quid has shown it is solvable in specific domains with focused effort. But I'm still looking for intuition to make them predictable & reliable techniques for the common case of structured data..