True, good point. That may be better for classification performance. But at least for visualization and interpretability purposes using NMF is extremely simple

True, good point. That may be better for classification performance. But at least for visualization and interpretability purposes using NMF is extremely simple and versatile (e.g. you can induce sparsity in the representation, setting the rank to be artificially low can cause high-level structure to “pop out”). That is, it gives you a few more knobs to turn than UMAP alone.