It is suggested that this principle underpins the effectiveness of machine learning algorithms in describing high-dimensional data sets by considering a few common features.
The manifold hypothesis is related to the effectiveness of nonlinear dimensionality reduction techniques in machine learning.
The major implications of this hypothesis is that The ability to interpolate between samples is the key to generalization in deep learning.
[5] An empirically-motivated approach to the manifold hypothesis focuses on its correspondence with an effective theory for manifold learning under the assumption that robust machine learning requires encoding the dataset of interest using methods for data compression.
From the perspective of dynamical systems, in the big data regime this manifold generally exhibits certain properties such as homeostasis: In a sense made precise by theoretical neuroscientists working on the free energy principle, the statistical manifold in question possesses a Markov blanket.