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Principal Component Analysis for Riemannian Functional Data and Bayes Classification
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Dai, Xiongtao. |
| 단체저자명 | University of California, Davis. Statistics. |
| 서명/저자사항 | Principal Component Analysis for Riemannian Functional Data and Bayes Classification. |
| 발행사항 | [S.l.] : University of California, Davis., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 99 p. |
| 소장본 주기 | School code: 0029. |
| ISBN | 9780438290761 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Hans-Georg Mueller. |
| 요약 | Functional data, or samples of smooth random functions observed over a continuum, have drawn extensive interest over the past 20 years. Classical linear functional data have been modeled in infinite-dimensional Hilbert spaces, where the infinite |
| 요약 | We consider an intrinsic Riemannian functional principal component analysis (RFPCA) for smooth Riemannian manifold-valued functional data. RFPCA is carried out by first mapping the manifold-valued data through Riemannian logarithm maps to linear |
| 요약 | Constructing Bayes classifiers for infinite dimensional functional data is difficult due to the fact that probability density functions do not exist for functional data. We approach this problem by considering density ratios of projections on a |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International80-01B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998734 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00028105 | 310 | 가야대학교// | 대출가능 |
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