Chapter 14. Maximizing similarity with t-SNE and UMAP
This chapter covers
- Understanding nonlinear dimension reduction
- Using t-distributed stochastic neighbor embedding
- Using uniform manifold approximation and projection
In the last chapter, I introduced you to PCA as our first dimension-reduction technique. While PCA is a linear dimension-reduction algorithm (it finds linear combinations of the original variables), sometimes the information in a set of variables can’t be extracted as a linear combination of these variables. In such situations, there are a number of nonlinear dimension-reduction algorithms we can turn to, such as t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP).
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