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Randomized Algorithms for Mining Massive Matrices: Design & Implementation at Terascale and Beyond
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Randomized Algorithms for Mining Massive Matrices: Design & Implementation at Terascale and Beyond. |
| 개인저자 | Iyer, Chander Jayaraman. |
| 단체저자명 | Rensselaer Polytechnic Institute. Computer Science. |
| 발행사항 | [S.l.]: Rensselaer Polytechnic Institute., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 120 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438206786 |
| 학위논문주기 | Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: Christopher D. Carothers |
| 요약 | Modern technological advancements and innovation has led to an explosive growth of data in various domains, ranging from physics and biological sciences to economics and social sciences. Research on mathematical libraries has been on the leading |
| 요약 | This dissertation is divided into three parts. In part I, we explore the behavior of randomized matrix algorithms based on the Blendenpik algorithm in a distributed memory setting. We show that a variant of the algorithm that uses a batchwise tr |
| 요약 | In part II of the dissertation, we explore the behavior of randomized block iterative solvers to compute low rank matrix approximations for dense terabyte sized matrices. We are particularly interested in the behavior of randomized block iterati |
| 요약 | In part III of the dissertation, we explore the behavior of large-scale kernel approximations using the Nystrom approach to solve the kernel ridge regression (KRR) problem. We demonstrate the scalability of one such Nystrom approximation approac |
| 일반주제명 | Computer science. Applied mathematics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
