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Open Access Green as soon as Postprint is submitted to ZB.
Schroedinger eigenmaps for the analysis of biomedical data.
IEEE Trans. Pattern Anal. Mach. Intell. 35, 1274-1280 (2013)
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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Times Cited
Times Cited
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4.795
9.183
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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
Language
english
Publication Year
2013
HGF-reported in Year
2013
ISSN (print) / ISBN
0162-8828
e-ISSN
1939-3539
Quellenangaben
Volume: 35,
Issue: 5,
Pages: 1274-1280
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
POF-Topic(s)
30505 - New Technologies for Biomedical Discoveries
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-551500-001
PubMed ID
23520264
WOS ID
WOS:000316126800019
Scopus ID
84875418338
Erfassungsdatum
2013-04-11