# \[ANN\] SimSearchManifoldLearning.jl: Non-linear dimensional reduction using SimilaritySearch (ManifoldLearning and UMAP)

**URL:** <https://discourse.julialang.org/t/ann-simsearchmanifoldlearning-jl-non-linear-dimensional-reduction-using-similaritysearch-manifoldlearning-and-umap/78416>\
**Category:** Package Announcements\
**Created:** [March 24, 2022, 4:38pm UTC](https://discourse.julialang.org/t/ann-simsearchmanifoldlearning-jl-non-linear-dimensional-reduction-using-similaritysearch-manifoldlearning-and-umap/78416 "2022-03-24T16:38:33Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Eric\_Sadit\_Tellez](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/eric_sadit_tellez/32/33615_2.png) [@Eric\_Sadit\_Tellez](https://discourse.julialang.org/u/Eric_Sadit_Tellez)\
**Post date:** [March 24, 2022, 4:38pm UTC](https://discourse.julialang.org/t/ann-simsearchmanifoldlearning-jl-non-linear-dimensional-reduction-using-similaritysearch-manifoldlearning-and-umap/78416/1 "2022-03-24T16:38:33Z")

</div>

I am glad to announce `SimSearchManifoldLearning,` which implements some methods and API to use `SimilaritySearch` with manifold learning methods. In particular, it implements the `knn` function for `ManifoldLearning` and related types. It also provides a `UMAP` implementation, based on [UMAP.jl](https://github.com/dillondaudert/UMAP.jl), that takes advantage of many `SimilaritySearch` features like multithreading and data-model independency; it supports string, sets, vectors, etc. under diverse distance functions. It also allows being more confident about the quality of the approximate `k` nearest neighbors effortlessly.

Note that many kinds of data will not work with `ManifoldLearning` since several methods work with certain distances or data models; also, its API is fixed to support matrix-like data. In any case, it can take advantage of multithreading and various distance functions.

Some Pluto notebooks are available [here](https://sadit.github.io/SimilaritySearchDemos/), in the `demonstrations` section.

I hope you find it helpful. Note that this is a work in progress. Suggestions and contributions are always welcome.

Regards,  
Eric