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Comment by sydthrowaway | Hacker News Reader

So what do people do nowadays

In general these are referred to as embedding techniques PCA/SVD is still common along with other linear methods like ICA, CCA, PLS t-sne is pretty popular statistically inspired with some manifold learning non-negative matrix factorization is useful recommender systems topologically inspired techniques are gaining traction, like UMAP, LLE the author notes neural embeddings, like autoencoders.

Around the time that this article was written I was putting the finishing touches on a neural autoencoder that was part of a search engine for patents. (The training was done with assembly language SIMD instructions so we weren't in a hurry to make any changes in the network, training algorithms, ...)

The search engine worked amazingly well. When we first demoed it we heard from the USPTO in the first week, they wanted to buy a license...

So what do you work on now if you were that ahead?

UMAP.

Obligatory Lior Pachter tweet (https://twitter.com/lpachter/status/1431325969411821572) [doesn't mention issue of initialization explictly but it's probably buried in the paper or the cited tweets]

Yeah. I was kind of infatuated with the UMAP paper when it first came up (and working through it to build intuition led me to persistent homology and other fun stuff), but it's far too complex to work as a "plug-in tool" that people naive to statistics can blindly use.

This is the power of ANOVA, CCA, linear regression and factor analysis, etc. It's much, much, much, much harder to shoot yourself in the foot, particularly with experimental data (usage in the social sciences is another bag of johnsons.)

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