The hypothesis suggests that the actual feature space of parameters which accurate encode the information of a dataset lie in smaller subspace compared to the actual parameters of the dataset.

The manifold hypothesis posits that many high-dimensional data sets that occur in the real world actually lie along low-dimensional latent manifolds inside that high-dimensional space.