Still some graphs are characterized by their spectra and several mathematical papers are devoted to this topic. In applications to computer sciences, spectral graph theory is considered as very strong. The benefit of using graph spectra in treating graphs is that eigenvalues and eigenvectors of ...
(or to) spectral theory. Schrödinger operators, scattering theory and resonances; eigenvalues: perturbation theory, asymptotics and inequalities; quantum graphs, graph Laplacians; pseudo-differential operators and semi-classical analysis; random matrix theory; the Anderson model and other random media; ...
Segmentation via Graph-Spectral Methods and Riemannian Geometry - Robles-Kelly - 2005 () Citation Context ...3.3 Note on Image Segmentation The Laplacian eigenmodes have been demonstrated to have important properties in the field of spectral graph theory [10,22,30,11] by providing a probabilistic...
Springer for Research & Development doi:10.1007/s10801-015-0590-5The spectral excess theoremWeakly distance-regular digraphsDistance-regular digraphsNormal digraphs05C5005E30The spectral excess theorem, a remarkable result due to Fiol and Garriga, states that a connected regular graph with \\(d+1\\...
This work evaluates the performance of the Complex Master Slave (CMS) method, that processes the spectra at the interferometer output of a spectral domain interferometry device without involving Fourier transforms (FT) after data acquisition. Reliability
This graph also depicts that unsupervised HCA clusters the cancerous spectra quite well by pathological criteria: all necrotic tissue classes are found together in one cluster along with keratin pearls, and all three SqCC grades and SCLC are differentiated from ADC. There is a clear distinction ...
The sensitivity-specificity trade-off involved in the detection is common in many biomedical problems and allows a user either to select the detection capability or the purity of obtained result as dictated by the graph for each class. This is a simplified representation as the actual class in ...
Spectral graph theory and applications in clustering and learning on manifolds Domain-specific aspects of using spectral approaches in applications Submitted papers should be in the ICML 2013 format with a maximum of 4 pages (not including references). Please e-mail your submission to spectralicml2013...
The spectral clustering methods based on the graph partitioning theory focus on finding the best cuts of a graph that optimize certain predefined criterion functions. The optimization of the criterion functions usually leads to the computation of singular vectors or Eigenvectors of certain graph affinity...
A novel approach is presented for synthetic aperture radar (SAR) image segmentation. By incorporating the advantages of maximally stable extremal regions (MSER) algorithm and spectral clustering (SC) method, the proposed approach provides effective and r