Multiscale Estimation of Intrinsic Dimensionality of Point Cloud Data and Multiscale Analysis of Dynamic Graphs

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