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Robust Algorithms for Low-Rank and Sparse Matrix Models
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Robust Algorithms for Low-Rank and Sparse Matrix Models. |
| 개인저자 | Moore, Brian E. |
| 단체저자명 | University of Michigan. Electrical Engineering. |
| 발행사항 | [S.l.]: University of Michigan., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 250 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438126145 |
| 학위논문주기 | Thesis (Ph.D.)--University of Michigan, 2018. |
| 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 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
