 Low-rank approximation

Low-rank approximation is a mathematical technique used in various fields such as machine learning, statistics, and signal processing to simplify data that is represented in high-dimensional space. The idea behind low-rank approximation is to approximate a given high-rank matrix (or a dataset) with a matrix of lower rank while retaining as much of the important information as possible.