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020 ▼a 9780438206786
035 ▼a (MiAaPQ)AAI10790439
035 ▼a (MiAaPQ)rpi:11299
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Iyer, Chander Jayaraman.
24510 ▼a Randomized Algorithms for Mining Massive Matrices: Design & Implementation at Terascale and Beyond.
260 ▼a [S.l.] : ▼b Rensselaer Polytechnic Institute., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 120 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: Christopher D. Carothers
5021 ▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0185.
650 4 ▼a Computer science.
650 4 ▼a Applied mathematics.
690 ▼a 0984
690 ▼a 0364
71020 ▼a Rensselaer Polytechnic Institute. ▼b Computer Science.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0185
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997572 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자