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Computing Topological Features for Data Analysis
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Shi, Dayu. |
| 단체저자명 | The Ohio State University. Computer Science and Engineering. |
| 서명/저자사항 | Computing Topological Features for Data Analysis. |
| 발행사항 | [S.l.] : The Ohio State University., 2017 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2017 |
| 형태사항 | 124 p. |
| 소장본 주기 | School code: 0168. |
| ISBN | 9780438098152 |
| 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 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T15000302 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00024994 | DP 004 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출불가(별치) |
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