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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)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter 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
Zeitschrift IEEE Transactions on Pattern Analysis and Machine Intelligence
Quellenangaben Band: 35, Heft: 5, Seiten: 1274-1280 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)