4 ms·
To anyone using t sne, consider giving UMAP a try. https://arxiv.org/abs/1802.03426 https://arxiv.org/abs/1802.03426 Equally accessible as tsne in Python.
by b_tterc_p 8y ago
To anyone using t sne, consider giving UMAP a try.
https://arxiv.org/abs/1802.03426 https://arxiv.org/abs/1802.03426
Equally accessible as tsne in Python.
- SubiculumCode 8y agoIs there an R implementation?
- deleted 8y ago[deleted]
- amrrs 8y agoYes https://www.kaggle.com/nulldata/tsne-alternate-umap-3d-viz-on-fashion-mnist https://www.kaggle.com/nulldata/tsne-alternate-umap-3d-viz-o... this has links to R UMAP implementation
- aflam 8y agoThanks for the article.I came across it as part of scikit-tda, but did not investigate. Looks promising! The TDA background is appealing, as well as runtime complexity.
- amelius 8y agoIn this tutorial the author reduces the size of the images from 28x28 to 10x10 to make the computation more tractable. Would that be necessary with UMAP too?
- b_tterc_p 8y agoT SNE and UMAP are techniques for dimensionality reduction. Both of them scale pretty well to high dimensionality problems. I think the author downsized the data because he wanted to provide an interactive model. If I were doing this I would probably just leave it as is for either method. The article I linked actually does benchmark on the same dataset though. For the full 70,000 images at 28x28, it claims 87 seconds for UMAP and 1450 for TSNE.