From f7efea048f798c141d04bf7952e890138a30593e Mon Sep 17 00:00:00 2001 From: ptresson <paul.tresson@ird.fr> Date: Mon, 2 Dec 2024 16:09:57 +0100 Subject: [PATCH] update docs with manifold module --- docs/source/tools.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/tools.md b/docs/source/tools.md index 8708800..8ad906e 100644 --- a/docs/source/tools.md +++ b/docs/source/tools.md @@ -66,7 +66,7 @@ The features produced by a deep learning encoder are often of high dimensionalit However, it can be cumbersome to deal with all these features and this high dimensionality feature space, especially when a majority are not really informative. Therefore, it is possible to reduce the dimensions of a raster using a variety of algorithms. We chose to rely on [scikit-learn](https://scikit-learn.org/) to provide the algorithms. -All algorighms available in the [decomposition](https://scikit-learn.org/stable/api/sklearn.decomposition.html) and the [cluster](https://scikit-learn.org/stable/api/sklearn.cluster.html) module that share a common API can be used. +All algorighms available in the [decomposition](https://scikit-learn.org/stable/api/sklearn.decomposition.html), [manifold](https://scikit-learn.org/stable/api/sklearn.manifold.html) and the [cluster](https://scikit-learn.org/stable/api/sklearn.cluster.html) module that share a common API can be used. Different algorithms have different arguments that can be passed. You can provide these as a json string in the corresponding field. -- GitLab