[godelski]

UMAP (and t-SNE) aren't the same as PCA. UMAP is pretty close to t-SNE and I think expanding PCA (Principle Component Analysis) and t-SNE (teacher Stochastic Neighbor Embedding) explain the difference. Neighbor embedding is a visualization technique and not the same as determining principle components. PCA preserves global properties while t-SNE and UMAP don't. They are good techniques for _visual_ dimensional reduction, but they aren't going to tell you the dominant eigenvectors of the data, or _dimensional reduction_. This is a bit of a pet peeve of mine.

There's some more in this SE post https://stats.stackexchange.com/questions/238538/are-there-c...