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High-Dimensional Estimation and Optimization With Multiple Structured Signals
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | High-Dimensional Estimation and Optimization With Multiple Structured Signals. |
| 개인저자 | Ha, Wooseok. |
| 단체저자명 | The University of Chicago. Statistics. |
| 발행사항 | [S.l.]: The University of Chicago., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 175 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438371569 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Chicago, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Rina Foygel Barber. |
| 요약 | Statistical recovery in high-dimensional statistics and signal processing often requests a determination of multiple structured signals from massive data. And depending on its application, either one or both of the signals may be of primarily i |
| 요약 | Chapter 2 describes a low-rank + sparse decomposition problem under data compression and we study rigorous statistical performance guarantee that is achievable using a joint convex optimization based estimator. It is well known that the convex r |
| 일반주제명 | Statistics. Medical imaging. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
