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Methods and applications of topological data analysis

著者: Jennifer Novak Kloke; G Carlsson; Steve Kerckhoff; Rafe Mazzeo; Stanford University. Department of Mathematics.
出版商: 2010.
论文: Thesis (Ph. D.)--Stanford University, 2010.
版本/格式:   硕士/博士论文 : 文献 : 硕士论文/博士论文 : 电子图书   计算机文档 : 英语
数据库:WorldCat
提要:
The focus of this dissertation is the development of methods for topological analysis as well as the application of topological tools to real world problems. The first half of the dissertation focuses on an algorithm for de-noising high-dimensional data for topological data analysis. This method significantly extends the applicability of many topological data analysis methods. In particular, this method extends the  再读一些...
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材料类型: 文献, 硕士论文/博士论文, 互联网资源
文件类型: 互联网资源, 计算机文档
所有的著者/提供者: Jennifer Novak Kloke; G Carlsson; Steve Kerckhoff; Rafe Mazzeo; Stanford University. Department of Mathematics.
OCLC号码: 652792734
注意: Submitted to the Department of Mathematics.
描述: 1 online resource.
责任: Jennifer Novak Kloke.

摘要:

The focus of this dissertation is the development of methods for topological analysis as well as the application of topological tools to real world problems. The first half of the dissertation focuses on an algorithm for de-noising high-dimensional data for topological data analysis. This method significantly extends the applicability of many topological data analysis methods. In particular, this method extends the use of persistent homology, a generalized notion of homology for discrete data points, to data sets that were previously inaccessible because of noise. The second half of this dissertation focuses on a method for using topology to simplify complex chemical structures and to define a metric to quantify similarity for use in screening large databases of chemical compounds. This method has shown very promising initial results in locating new materials for efficiently separating carbon dioxide from the exhaust of coal-burning power plants.

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