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Unsupervised Methods on Structured Data
Unsupervised Methods on Structured Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104646
- ISBN
- 9798280757837
- DDC
- 310
- 저자명
- Vinas, Luciano.
- 서명/저자
- Unsupervised Methods on Structured Data
- 발행사항
- [Sl] : University of California, Los Angeles, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 228 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Amini, Arash A.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2025.
- 초록/해제
- 요약Classical unsupervised algorithms, such as k-means and PCA, utilize a simple generative model where the sampling distribution is determined by a collection of unobserved, latent features. While this paradigm is powerful, it has the following consequence for applied settings: any structured trend in the data must be explained by the latent features and the assumptions therein. This requirement complicates the analysis of structured data sources, such as images, videos, and networks, especially when the latent features of interest do not govern every structured aspect of the data.In this work, we consider scenarios where the latent features may be partially decoupled from the structure of the data. Under this new setting we develop new algorithmic improvements and insights for the following problems:Tissue intensity recovery for contaminated MRIs, where each pixel intensity is determined by an underlying tissue type and a spatially varying gain field.Semi-supervised node classification with graph aggregated features, where nodes are assumed to follow a community-based structure.
- 일반주제명
- Statistics
- 일반주제명
- Information technology
- 키워드
- Clustering
- 키워드
- Structured data
- 기타저자
- University of California, Los Angeles Statistics 0891
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798280757837
■035 ▼a(MiAaPQ)AAI32114654
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aVinas, Luciano.
■24510▼aUnsupervised Methods on Structured Data
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a228 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Amini, Arash A.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2025.
■520 ▼aClassical unsupervised algorithms, such as k-means and PCA, utilize a simple generative model where the sampling distribution is determined by a collection of unobserved, latent features. While this paradigm is powerful, it has the following consequence for applied settings: any structured trend in the data must be explained by the latent features and the assumptions therein. This requirement complicates the analysis of structured data sources, such as images, videos, and networks, especially when the latent features of interest do not govern every structured aspect of the data.In this work, we consider scenarios where the latent features may be partially decoupled from the structure of the data. Under this new setting we develop new algorithmic improvements and insights for the following problems:Tissue intensity recovery for contaminated MRIs, where each pixel intensity is determined by an underlying tissue type and a spatially varying gain field.Semi-supervised node classification with graph aggregated features, where nodes are assumed to follow a community-based structure.
■590 ▼aSchool code: 0031.
■650 4▼aStatistics
■650 4▼aInformation technology
■653 ▼aClustering
■653 ▼aGraph aggregation
■653 ▼aUnsupervised learning
■653 ▼aStructured data
■653 ▼aClassical unsupervised algorithms
■690 ▼a0463
■690 ▼a0489
■690 ▼a0800
■71020▼aUniversity of California, Los Angeles▼bStatistics 0891.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358335▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
