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Schroedinger Eigenmaps for the Analysis of Biomedical Data
May 2013 (vol. 35 no. 5)
pp. 1274-1280
W. Czaja, Dept. of Math., Univ. of Maryland, College Park, MD, USA
M. Ehler, Helmholtz Zentrum Munchen, German Res. Center for Environ. Health, Neuherberg, Germany
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.
Index Terms:
retinal recognition,eigenvalues and eigenfunctions,graph theory,learning (artificial intelligence),medical image processing,schroedinger Eigenmaps,multispectral retinal images,graph Schroedinger operators,semisupervised manifold learning,SE,biomedical data analysis,Laplace equations,Vectors,Manifolds,Kernel,Eigenvalues and eigenfunctions,Biomedical imaging,Labeling,manifold learning,Schroedinger Eigenmaps,Laplacian Eigenmaps,Schroedinger operator on a graph,barrier potential,dimension reduction
W. Czaja, M. Ehler, "Schroedinger Eigenmaps for the Analysis of Biomedical Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1274-1280, May 2013, doi:10.1109/TPAMI.2012.270
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