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Czaja, W.* ; Ehler, M.

Schroedinger eigenmaps for the analysis of biomedical data.

IEEE Trans. Pattern Anal. Mach. Intell. 35, 1274-1280 (2013)
Publ. Version/Full Text DOI
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Schroedinger Eigenmaps ; Laplacian Eigenmaps ; Schroedinger Operator On A Graph ; Barrier Potential ; Dimension Reduction ; Manifold Learning; Nonlinear Dimensionality Reduction ; Macular Degeneration ; Geometric Framework ; Bruchs Membrane ; Eye Disease ; Drusen ; Regularization ; Segmentation ; Diagnosis ; Tool
ISSN (print) / ISBN 0162-8828
e-ISSN 1939-3539
Quellenangaben Volume: 35, Issue: 5, Pages: 1274-1280 Article Number: , Supplement: ,
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Reviewing status Peer reviewed