Dimensionality Reduction with t-SNE in Python
t-distributed stochastic neighborhood embedding (t-SNE) is a unsupervised non-linear dimensionality reduction and data visualization technique.
The math behind t-SNE is quite complex but the idea is simple.
It embeds the points from a higher dimension to a lower dimension trying to preserve the neighborhood of that point.
I compared PCA and t-SNE methodology for dimensionality reduction.
Then I demonstrated how to use and tune t-SNE model in Python.
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The Python code is uploaded into https://github.com/AIMLModeling/tSNE