Have you heard of Uniform Manifold Approximation and Projection (UMAP)?
UMAP (Uniform Manifold Approximation and Projection) is a novel
manifold learning technique for non-linear dimension reduction. UMAP is
constructed from a theoretical framework based in Riemannian geometry
and algebraic topology. The result is a practical scalable algorithm
that applies to real world data. The UMAP algorithm is competitive
with t-SNE for visualization quality, and arguably preserves more of
the global structure with superior run time performance. Furthermore,
UMAP as described has no computational restrictions on embedding
dimension, making it viable as a general purpose dimension reduction
technique for machine learning.
Check their code and original paper for list of pros and cons, it is super easy to use.
Quick Facts: UMAP can handle large datasets and is faster than t-SNE and also supports fitting to sparse matrix data, and contrary to t-SNE, a general purpose dimension reduction technique, meaning that not only it can be used for visualisation but also for reducing the feature space for feeding into other machine learning models.
Concrete Examples: I have benchmarked the method and compared it against some other dimensionality reduction techniques benchmark notebook, if interested to have a quick look and a jump start.