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Robust Algorithms for Low-Rank and Sparse Matrix Models
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Moore, Brian E. |
| 단체저자명 | University of Michigan. Electrical Engineering. |
| 서명/저자사항 | Robust Algorithms for Low-Rank and Sparse Matrix Models. |
| 발행사항 | [S.l.] : University of Michigan., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 250 p. |
| 소장본 주기 | School code: 0127. |
| ISBN | 9780438126145 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Raj Rao Nadakuditi. |
| 요약 | Data in statistical signal processing problems is often inherently matrix-valued, and a natural first step in working with such data is to impose a model with structure that captures the distinctive features of the underlying data. Under the rig |
| 요약 | This thesis focuses on developing new robust PCA algorithms that advance the state-of-the-art in several key respects. First, we develop a theoretical understanding of the effect of outliers on PCA and the extent to which one can reliably reject |
| 일반주제명 | Electrical engineering. Statistics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T15000515 |
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인쇄
| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00025157 | DP 621.3 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출불가(별치) |
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