Isomap

Isomap is built on the assumption that high-dimensional data lies on a lower-dimensional manifold. It estimates geodesic distances through neighborhood relationships and preserves that structure in a lower-dimensional embedding. This can be especially meaningful in problems where nonlinear geometry matters. However, the quality of the neighborhood graph and the level of noise strongly influence performance.