Tried Parametric UMAP but its performance does not seem to be as good as Non parametric one even after training on large data of around 20k examples in supervised fashion.

Nobody has claimed this yet.

Tried Parametric UMAP but its performance does not seem to be as good as the Nonparametric one even after training on large data of around 20k examples in a supervised fashion.
I am fully convinced that parametric is the future due to its learning from previous data and hence global learning like pre-trained models and hence transfer learning.

Can anyone help me if I am doing any basic mistakes?

**Problem:**
I am trying to perform text clustering using Sentence Transformers embedding of 748 dimensions.

**Method:**
I have supervised data of around 20000 samples belong to 1200 labels. I am performing PCA and then using parametric supervised UMAP (default encoder), trained and saved the model.

But after performing on test data of around 1500 samples, using the saved embedder, and using fit and transform, cluster assignment is not good as nonparametric UMAP.

Can someone tell me if I am doing anything wrong?

The issue describes PCA followed by supervised parametric UMAP with the default encoder, then applying a saved embedder with fit and transform to test data. Start by reproducing that training and evaluation flow on the stated 20,000 training and 1,500 test samples, and compare it with nonparametric UMAP. Done means identifying whether the reported clustering difference comes from the workflow or from parametric UMAP behavior.

Written by the indexing model from the issue text.