A random walks view of spectral segmentation Export

@misc{citeulike:1142015, abstract = {We present a new view of clustering and segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This view shows that spectral methods for clustering and segmentation have a probabilistic foundation. We prove that the Normalized Cut method arises naturally from our framework and we provide a complete characterization of the cases when the Normalized Cut...}, author = {Meila, M. and Shi, J.}, citeulike-article-id = {1142015}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.1501}, keywords = {graph-partitioning, random-walk, spectral-clustering, yrbcn}, posted-at = {2008-06-10 19:16:40}, priority = {2}, title = {A random walks view of spectral segmentation}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.1501}, year = {2001} }

TY - GEN ID - citeulike:1142015 L3 - citeulike-article-id:1142015 N2 - We present a new view of clustering and segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This view shows that spectral methods for clustering and segmentation have a probabilistic foundation. We prove that the Normalized Cut method arises naturally from our framework and we provide a complete characterization of the cases when the Normalized Cut... TI - A random walks view of spectral segmentation KW - graph-partitioning KW - random-walk KW - spectral-clustering KW - yrbcn AU - Meila, M AU - Shi, J PY - 2001/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.1501 ER -