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Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications. |
| 개인저자 | Tao, Shaozhe. |
| 단체저자명 | University of Minnesota. Industrial and Systems Engineering. |
| 발행사항 | [S.l.]: University of Minnesota., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 189 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438168909 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: Shuzhong Zhang |
| 요약 | Many problems in machine learning can be formulated using optimization models with constraints that are well structured. Driven in part by such applications, the need to solve very large scale optimization models is pushing the performance limit |
| 요약 | First, we study popular scalable methods on sparse structured models, including alternating direction method of multipliers, coordinate descent method, proximal gradient method and accelerated proximal gradient method. In contrast to many global |
| 요약 | Next we move on to group sparse structured model. We develop an inverse covariance estimator that can regularize for overlapping group sparsity, and provide better estimates, especially when the dimension size is much larger than the number of s |
| 요약 | Finally, we explore a certain low-rank structure in tensor. We construct the connection between the low-rank property in tensor and the group sparsity in its factor matrices. This provides a way to find a low-rank tensor decomposition via a regu |
| 일반주제명 | Industrial engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
