Iterative Non-linear Dimensionality Reduction with Manifold Sculpting

Many algorithms have been recently developed for reducing dimensionality by projecting data onto an intrinsic nonlinear manifold. Unfortunately, existing algorithms often lose significant precision in this transformation. Manifold Sculpting is a new algorithm that iteratively reduces dimensionality by simulating surface tension in local neighborhoods. We present several experiments that show Manifold Sculpting yields more accurate results than existing algorithms with both generated and natural data-sets. Manifold Sculpting is also able to benefit from both prior dimensionality reduction efforts.