A low rank approximation is a form of dimensionality reduction where we come up with a lower-dimensional estimation of a given matrix or dataset. It can be framed as an optimization problem, where we try to minimize the error between the actual data and the approximation.
Real world analogies:
- Human intuition
- vertical slice
- spitpaint
- cognitive scaffolding