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Randomized Algorithms for Mining Massive Matrices: Design & Implementation at Terascale and Beyond

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자료유형E-Book
개인저자Iyer, Chander Jayaraman.
단체저자명Rensselaer Polytechnic Institute. Computer Science.
서명/저자사항Randomized Algorithms for Mining Massive Matrices: Design & Implementation at Terascale and Beyond.
발행사항[S.l.] : Rensselaer Polytechnic Institute., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항120 p.
소장본 주기School code: 0185.
ISBN9780438206786
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: Christopher D. Carothers
요약Modern 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
요약This 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
요약In 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
요약In 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
일반주제명Computer science.
Applied mathematics.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14997572

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