Discretized Gradient Flow for Manifold Learning in the Space of Embeddings

no code implementations • 25 Jan 2019 • Dara Gold, Steven Rosenberg

Implementing a discretized version of gradient descent for $P:\mathcal{E}\to {\mathbb R}$, a penalty function that scores an embedding $\phi \in \mathcal{E}$, requires estimating how far we can move in a fixed direction -- the direction of one gradient step -- before leaving the space of smooth embeddings.

Dimensionality Reduction Differential Geometry