Issue No. 05 - May (2013 vol. 35)
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.
Laplace equations, Vectors, Manifolds, Kernel, Eigenvalues and eigenfunctions, Biomedical imaging, Labeling
W. Czaja and M. Ehler, "Schroedinger Eigenmaps for the Analysis of Biomedical Data," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 5, pp. 1274-1280, 2013.