manifold learning

This term is a specialized technical expression used primarily in data science, topology, and machine learning. It describes the process of uncovering the intrinsic geometric structure of a dataset, operating on the premise that the observed high-dimensional data is actually a projection of a simpler, lower-dimensional shape.

In a professional or academic register, it is used to distinguish non-linear dimensionality reduction techniques from linear methods like Principal Component Analysis (PCA). Because it refers to a specific theoretical framework and methodology, it is treated as an uncountable mass noun in technical discourse.