Svd, Dimensionality Reduction, Latent Semantic Indexing, Machine Learning, Similarity Search.
so on… If a data scientist can find those most important pieces, stop there, approximate the whole data with those important parts, that’s it”. You can do the magic. The basic idea behind SVD is, every matrix can be decomposed as a product of 3 matrices. A “rotation matrix” followed by a “stretch”