Schroedinger Eigenmaps for the Analysis of Bio-Medical Data

Abstract

We introduce Schroedinger Eigenmaps, a new semi-supervised manifold learning and recovery technique. This method is based on an implementation of graph Schroedinger operators with appropriately constructed barrier potentials as carriers of labeled information. We use our approach for the analysis of standard bio-medical datasets and new multispectral retinal images.

References in corpus (3)

• Consistency of spectral clustering
• Manifold Learning: The Price of Normalization
• Spectral descriptors for deformable shapes

Cited by in corpus (1)

• Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics