Separating Features from Noise with Persistence and Statistics

Separating Features from Noise with Persistence and Statistics

<p>In this thesis, we explore techniques in statistics and persistent homology, which detect features among data sets such as graphs, triangulations and point cloud. We accompany our theorems with algorithms and experiments, to demonstrate their effectiveness in practice.</p><p><...

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Bibliographic Details
Main Author: Wang, Bei
Other Authors: Edelsbrunner, Herbert
Published: 2010
Subjects:
Computer Science
Theoretical Mathematics
Statistics
clustering
computational geometry
computational topology
persistence homology
spatial scan statistics
stratification
Online Access:http://hdl.handle.net/10161/2982

Internet

http://hdl.handle.net/10161/2982

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