Efficient dimensionality reduction for large dataset
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.