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.
Publication types
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Research Support, N.I.H., Extramural
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Research Support, N.I.H., Intramural
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Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
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Algorithms*
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Artificial Intelligence*
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Biomedical Research
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Breast Neoplasms / classification
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Data Interpretation, Statistical
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Data Mining / methods*
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Databases, Factual*
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Female
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Heart Diseases / classification
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Humans
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Pattern Recognition, Automated / methods
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Retinal Diseases / diagnosis
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Retinal Diseases / pathology