CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.05 - May
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,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 & Machine Intelligence, vol.35, no. 5, pp. 1274-1280, May 2013, doi:10.1109/TPAMI.2012.270