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Clustering and Embedding Using Commute Times
2007
IEEE Transactions on Pattern Analysis and Machine Intelligence
This paper exploits the properties of the commute time between nodes of a graph for the purposes of clustering and embedding, and explores its applications to image segmentation and multi-body motion tracking. Our starting point is the lazy random walk on the graph, which is determined by the heatkernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterize the random walk using the commute time (i.e. the expected time taken for a random walk to travel
doi:10.1109/tpami.2007.1103
pmid:17848771
fatcat:ipjnswb4znhbxkwct23iycgztu