Parametric UMAP embeddings for representation and semi-supervised learning — mentioned by lmcinnes

Sat, 03 Jul 2021 22:17:16 GMT

If this is a thing you want to be able to do efficiently then ParametricUMAP (see [docs](https://umap-learn.readthedocs.io/en/latest/parametric_umap....) and [the paper](https://arxiv.org/abs/2009.12981)) will be very effective. It uses a neural network to learn a mapping directly from data to embedding space using a UMAP loss. Pushing new data through is only slightly more expensive than PCA, so being part of an inference pipeline is fine.

Paper and discussion history. A mention is not an endorsement.

Parametric UMAP embeddings for representation and semi-supervised learning · HackerPapers discussions

Parametric UMAP embeddings for representation and semi-supervised learning — mentioned by lmcinnes