Schroedinger Eigenmaps for the Analysis of Biomedical Data

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 analys...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2013-05, Vol.35 (5), p.1274-1280
Hauptverfasser:	Czaja, W., Ehler, M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial Intelligence barrier potential Biological and medical sciences Biomedical imaging Biomedical Research Breast Neoplasms - classification Computer science control theory systems Data Interpretation, Statistical Data Mining - methods Data processing. List processing. Character string processing Databases, Factual dimension reduction Eigenvalues and eigenfunctions Exact sciences and technology Female Heart Diseases - classification Humans Information retrieval. Graph Investigative techniques, diagnostic techniques (general aspects) Kernel Labeling Laplace equations Laplacian Eigenmaps Learning and adaptive systems manifold learning Manifolds Medical sciences Memory organisation. Data processing Ophthalmology Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques Pattern Recognition, Automated - methods Retinal Diseases - diagnosis Retinal Diseases - pathology Schroedinger Eigenmaps Schroedinger operator on a graph Software Theoretical computing Vectors
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Zusammenfassung:	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.
ISSN:	0162-8828 1939-3539 2160-9292
DOI:	10.1109/TPAMI.2012.270