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Computing Topological Features for Data Analysis
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Computing Topological Features for Data Analysis. |
| 개인저자 | Shi, Dayu. |
| 단체저자명 | The Ohio State University. Computer Science and Engineering. |
| 발행사항 | [S.l.]: The Ohio State University., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 124 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438098152 |
| 학위논문주기 | Thesis (Ph.D.)--The Ohio State University, 2017. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: Tamal Dey |
| 요약 | Topological data analysis (TDA) provides a new methodology to data analysis problems. It captures intrinsic topological structures in data, which can then offer useful guidelines for other data analysis approaches. One main task in TDA is to ext |
| 요약 | I will present a focused study during my PhD research on broadening applicability of the idea of persistence in data analysis in two fronts, to explore novel ways of applying persistent homology for qualitative data analysis and to study the com |
| 요약 | In the first direction, we applied persistent homology to a special kind of data, called metric graphs. A metric graph offers one of the simplest yet still meaningful ways to represent the non-linear structure hidden behind the data. Thus, compa |
| 요약 | In the second part, we consider the more general case, high-dimensional point cloud data. To extract topological features of a point cloud data sampled from a metric space, a sequence of Rips complexes built on P indexed by a scale parameter is |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
