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Scalable and Ensemble Learning for Big Data
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Scalable and Ensemble Learning for Big Data. |
| 개인저자 | Traganitis, Panagiotis. |
| 단체저자명 | University of Minnesota. Electrical/Computer Engineering. |
| 발행사항 | [S.l.]: University of Minnesota., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 140 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085610308 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Giannakis, Georgios B. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | The turn of the decade has trademarked society and computing research with a ``data deluge.'' As the number of smart, highly accurate and Internet-capable devices increases, so does the amount of data that is generated and collected. While this sheer amount of data has the potential to enable high quality inference, and mining of information, it introduces numerous challenges in the processing and pattern analysis, since available statistical inference and machine learning approaches do not necessarily scale well with the number of data and their dimensionality. In addition to the challenges related to scalability, data gathered are often noisy, dynamic, contaminated by outliers or corrupted to specifically inhibit the inference task. Moreover, many machine learning approaches have been shown to be susceptible to adversarial attacks. At the same time, the cost of cloud and distributed computing is rapidly declining. Therefore, there is a pressing need for statistical inference and machine learning tools that are robust to attacks and scale with the volume and dimensionality of the data, by harnessing efficiently the available computational resources.This thesis is centered on analytical and algorithmic foundations that aim to enable statistical inference and data analytics from large volumes of high-dimensional data. The vision is to establish a comprehensive framework based on state-of-the-art machine learning, optimization and statistical inference tools to enable truly large-scale inference, which can tap on the available (possibly distributed) computational resources, and be resilient to adversarial attacks. The ultimate goal is to both analytically and numerically demonstrate how valuable insights from signal processing can lead to markedly improved and accelerated learning tools.To this end, the present thesis investigates two main research thrusts: i) Large-scale subspace clustering |
| 일반주제명 | Electrical engineering. Computer science. Mathematics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
