Conference Program

Generalizable Manifold Learning for Dimensional Reduction (310095)

*Jungeum Kim, Purdue University  
Xiao Wang, Purdue University  

Keywords: Manifold learning, Dimensional reduction, Visualization, Deep learning

In this paper, we develop an inductive manifold learning algorithm that is ideal for dimensional reduction and visualization with deep neural net- works (DNNs), which generalizes on the unseen test dataset. Our method preserves both global and local information by approximating geodesic distances on the data manifold and preserving them. Theoretical justification on the distance estimator is provided with a careful analysis of the topological structure of the manifold.