[wenc]

A lot of relationships are (locally) linear so this isn’t as restrictive as it might seem. Many real-life productionized applications are based on it. Like linear regression, it has its place.

T-SNE is good for visualization and for seeing class separation, but in my experience, I haven’t found it to work for me for dimensionality reduction per se (maybe I’m missing something). For me, it’s more of a visualization tool.

On that note, there’s a new algorithm that improves on T-SNE called PaCMAP which preserves local and global structures better. https://github.com/YingfanWang/PaCMAP

[a_bonobo]

There's also Bonsai, it's parameter-free and supposedly 'better' than t-SNE, but it's clearly aimed at visualisation purposes (except that in Bonsai trees, distances between nodes are 'real' which is usually not the case in t-SNE)

https://www.biorxiv.org/content/10.1101/2025.05.08.652944v1....

[energy123]

I'd add that PCA/OLS is linear in the functional form (linear combination), but the input variables can be non-linear (X_new := X_{old,1}*X_{old,2}^2), so if the non-linearities are simple, then basic feature engineering to strip out the non-linearities before fitting PCA/OLS may be acceptable.