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Schroedinger Eigenmaps for the analysis of biomedical data
1Department of Mathematics, University of Maryland, College Park, MD 20742, USA. wojtek@math.umd.edu
Abstract:
We introduce Schroedinger Eigenmaps (SE), 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 biomedical datasets and new multispectral retinal images.
