3 ms·
I'm just wondering if leaving out tSNE was intentional? I'm a big fan of UMAP too. Just created this ^1 Kaggle Kernel a couple of days back to show how UMAP wor
by amrrs 7y ago
I'm just wondering if leaving out tSNE was intentional? I'm a big fan of UMAP too. Just created this ^1 Kaggle Kernel a couple of days back to show how UMAP works on Kannada MNIST data.
I also think PCA still rules in a lot of cases like when people try to create a new index representative of a bunch of numeric variables
[1] https://www.kaggle.com/nulldata/umap-dim-reduction-viz-on-kannada-mnist https://www.kaggle.com/nulldata/umap-dim-reduction-viz-on-ka...
- nestorD 7y agoI am not the OP but I would say that UMAP is a clear superset of t-SNE being able to do the same thing but faster and with a better conservation of large scale distances.
- beagle3 7y agot-SNE doesn’t really tell you how to apply its transformation to new data. (UMAP and PCA do). As a result, t-sne is great for visualization or for “we know everything from the start” data, but not as useful in most data processing pipelines.
- autokad 7y agoNice kernel, hope to use this in the short future