Neumann eigenmaps for landmark embedding

Shashank Sule, Wojciech Czaja

Session: General

Keywords: Manifold learning, Diffusion maps, Spectral graph theory

TL;DR: A new landmark-based manifold learning technique which embeds data through Neumann eigenvectors of a subgraph

Abstract: We present Neumann eigenmaps (NeuMaps), a novel approach for enhancing the standard diffusion map embedding using $landmarks$—distinguished samples within the dataset. By interpreting these landmarks as a subgraph of the larger data graph, NeuMaps are obtained via the eigendecomposition of a renormalized Neumann Laplacian. We show that NeuMaps offer two key advantages: (1) they provide a computationally efficient embedding that accurately recovers the diffusion distance associated with the reflecting random walk on the subgraph, and (2) they naturally incorporate the Nystr\"om extension within the diffusion map framework through the discrete Neumann boundary condition. Through examples in digit classification and molecular dynamics, we demonstrate that NeuMaps not only improve upon existing landmark-based embedding methods but also enhance the stability of diffusion map embeddings to the removal of highly significant points.

Submission Number: 92