Schroedinger Eigenmaps for the Analysis of Bio-Medical Data

TL;DR

Schroedinger Eigenmaps is a semi-supervised manifold learning technique utilizing graph Schroedinger operators with barrier potentials, applied to bio-medical datasets and multispectral retinal images for data analysis.

Contribution

The paper introduces Schroedinger Eigenmaps, a novel semi-supervised manifold learning method using graph Schroedinger operators with barrier potentials for biomedical data analysis.

Findings

Effective analysis of biomedical datasets

Successful application to multispectral retinal images

Demonstrates improved data recovery

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.