In general these are referred to as embedding techniques PCA/SVD is still common along with other linear methods like ICA, CCA, PLS t-sne is pretty popular statistically inspired with some manifold learning non-negative matrix factorization is useful recommender systems topologically inspired techniques are gaining traction, like UMAP, LLE the author notes neural embeddings, like autoencoders.