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dc.contributor.author Lee, Jason
dc.date.accessioned 2010-04-30T19:56:50Z
dc.date.available 2010-04-30T19:56:50Z
dc.date.issued 2010-04-30T19:56:50Z
dc.identifier.uri http://hdl.handle.net/10161/2225
dc.description Honors Thesis en_US
dc.description.abstract In the first part of the thesis, we study the problem of estimating the intrinsic dimension of point cloud data sampled from a noisy manifold. We show that a multiscale algorithm succeeds with high probability. The second part of this thesis studies Dynamic Networks using a multiscale methodology. We propose a novel multiscale algorithm to analyze the dynamics of graphs and networks. en_US
dc.format.extent 1455978 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.subject multiscale en_US
dc.subject manifold en_US
dc.subject dimension en_US
dc.subject with high probability en_US
dc.subject point cloud en_US
dc.title Multiscale Estimation of Intrinsic Dimensionality of Point Cloud Data and Multiscale Analysis of Dynamic Graphs en_US
dc.department Mathematics en_US

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