Slides for my @SciPyConf talk this morning on UMAP can be found here: https://t.co/XsZVuRxb5a
— Leland McInnes (@leland_mcinnes) July 12, 2018
Slides for my @SciPyConf talk this morning on UMAP can be found here: https://t.co/XsZVuRxb5a
— Leland McInnes (@leland_mcinnes) July 12, 2018
UMAP version 0.3 is now available. You can now add new data to an existing embedding, embed using labelled data, or use both features for metric learning. Documentation is on readthedocs: https://t.co/ZFaOHrPti4. pic.twitter.com/CqiuZFbmCQ
— Leland McInnes (@leland_mcinnes) July 12, 2018
umap: Uniform Manifold Approximation and Projection for dimensionality reduction. #Python #DataScience #MachineLearning
— Randy Olson (@randal_olson) July 13, 2018
Claims to have several advantages over tSNE.https://t.co/0N06f8ktcn pic.twitter.com/Bh5ogX6dTQ
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction by Leland McInnes. https://t.co/nSt7USWsXv. This talk presents a new approach to dimension reduction called UMAP.
— Python Software (@ThePSF) December 3, 2018
An updated and significantly expanded version of our UMAP paper is now on arXiv: https://t.co/bq4WuzuXvB
— Leland McInnes (@leland_mcinnes) December 8, 2018
More explanation, algorithm descriptions, and more experiments looking at stability, and working directly on high dimensional data -- as high as 1.8 million dimensional data! pic.twitter.com/Frsqwj7GmP