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020 ▼a 9780438098152
035 ▼a (MiAaPQ)AAI10901874
035 ▼a (MiAaPQ)OhioLINK:osu1512079255367232
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 004
1001 ▼a Shi, Dayu.
24510 ▼a Computing Topological Features for Data Analysis.
260 ▼a [S.l.]: ▼b The Ohio State University., ▼c 2017.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2017.
300 ▼a 124 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: Tamal Dey
5021 ▼a Thesis (Ph.D.)--The Ohio State University, 2017.
520 ▼a 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
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0168.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a The Ohio State University. ▼b Computer Science and Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0168
791 ▼a Ph.D.
792 ▼a 2017
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000302 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033