Manifold hypothesis

The Manifold Hypothesis is a concept in machine learning and data analysis that suggests that high-dimensional data, which often appears to be spread out in a vast space, actually lies on a lower-dimensional manifold. This means that even though data points may exist in a high-dimensional space, they often occupy a space of much lower dimension within that high-dimensional space.