UMAP Dimensionality Reduction in Python
UMAP is a dimensionality reduction technique that assumes the available data samples are evenly distributed across a topological space, which can be approximated from these finite data samples and mapped to a lower-dimensional space.
t-SNE preserves local structure in the data, while UMAP preserves both local and most of the global structure in the data.
I compared UMAP with t-SNE and summarized the features of UMAP.
Then I demonstrated how to use UMAP in Python.
You are welcome to provide your comments and subscribe to my YouTube channel.
The Python code is uploaded into https://github.com/AIMLModeling/UMAP
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