From bb0199bc210395d679e426d65545b550c2ffb972 Mon Sep 17 00:00:00 2001 From: Stefan Hahmann Date: Fri, 6 Dec 2024 16:05:06 +0100 Subject: [PATCH] Remove t-SNE from docs --- README.md | 16 +--------------- doc/dimensionalityreduction/umap_dialog.png | Bin 32268 -> 48301 bytes 2 files changed, 1 insertion(+), 15 deletions(-) diff --git a/README.md b/README.md index 0d7538187..ec003a0fb 100644 --- a/README.md +++ b/README.md @@ -304,14 +304,11 @@ Tree2 ## Dimensionality reduction For visualizing high-dimensional data, e.g. in two dimensions, potentially getting more insights into your data, you can -reduce the dimensionality of the measurements, using these algorithms: +reduce the dimensionality of the measurements, using this algorithm: * UMAP * [Uniform Manifold Approximation Projection (UMAP)](https://arxiv.org/abs/1802.03426) * [UMAP Python implementation](https://umap-learn.readthedocs.io/en/latest/) -* t-SNE - * [t-distributed Stochastic Neighbor Embedding (t-SNE)](https://lvdmaaten.github.io/tsne/) - * [t-SNE Python implementation](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) ### Usage @@ -363,17 +360,6 @@ By default, all measurements are selected in the box. representation. This parameter controls how tightly UMAP is allowed to pack points together. Further reading: [Minimum Distance](https://umap-learn.readthedocs.io/en/latest/parameters.html#min-dist). -#### t-SNE Parameters - -* Perplexity: The perplexity is related to the number of nearest neighbors that are used in other manifold learning - algorithms. Larger datasets usually require a larger perplexity. The recommended range is between 5 and 50. - Further - reading: [Perplexity](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE). -* Maximum number of iterations: The maximum number of iterations for the optimization. The default is 1000. More - iterations will give more accurate results, but will also take longer to compute. - Further - reading: [Maximum Number of Iterations](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html#sklearn.manifold.TSNE). - When you are done with the selection, click on `Compute`. 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