title: Schroedinger Eigenmaps for the analysis of biomedical data.
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.
authors.affiliation: Department of Mathematics, University of Maryland, College Park, MD 20742, USA. wojtek@math.umd.edu
authors.forename: Wojciech
authors.initials: W
authors.lastname: Czaja
authors.forename: Martin
authors.initials: M
authors.lastname: Ehler
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D035843:Biomedical Research; D001943:Breast Neoplasms / Q000145:classification; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005260:Female; D006331:Heart Diseases / Q000145:classification; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods; D012164:Retinal Diseases / Q000175:diagnosis / Q000473:pathology