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Unsupervised Learning: Evaluation, Distributed Setting, and Privacy
Unsupervised Learning: Evaluation, Distributed Setting, and Privacy
상세정보
- 자료유형
- 학위논문
- Control Number
- 0014997192
- International Standard Book Number
- 9780438206403
- Dewey Decimal Classification Number
- 004
- Main Entry-Personal Name
- Tsikhanovich, Maksim.
- Publication, Distribution, etc. (Imprint
- [Sl] : Rensselaer Polytechnic Institute, 2018
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2018
- Physical Description
- 134 p
- General Note
- Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
- General Note
- Adviser: Malik Magdon-Ismail.
- Dissertation Note
- Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
- Summary, Etc.
- 요약Chapter 1 is an overview of topic modeling as a set of unsupervised learning tasks. We present the Latent Dirichlet Allocation (LDA) model, and show how k-means as well as non-negative matrix factorization (NMF) can also be interpreted as topic
- Summary, Etc.
- 요약In Chapter 2 we present two algorithms for the data-distributed non-negative matrix factorization (NMF) task, and one for the singular value decomposition (SVD). In the offline setting, M parties have already computed NMF models of their local d
- Summary, Etc.
- 요약In Chapter 3 we study empirical measures of Distributional Differential Privacy. We want to measure to what extent one participant in a distributed computation can correctly identify the presence of a single document in another participant's dat
- Subject Added Entry-Topical Term
- Computer science
- Added Entry-Corporate Name
- Rensselaer Polytechnic Institute Computer Science
- Host Item Entry
- Dissertation Abstracts International. 79-12B(E).
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:555467
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■1001 ▼aTsikhanovich, Maksim.
■24510▼aUnsupervised Learning: Evaluation, Distributed Setting, and Privacy
■260 ▼a[Sl]▼bRensselaer Polytechnic Institute▼c2018
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2018
■300 ▼a134 p
■500 ▼aSource: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
■500 ▼aAdviser: Malik Magdon-Ismail.
■5021 ▼aThesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
■520 ▼aChapter 1 is an overview of topic modeling as a set of unsupervised learning tasks. We present the Latent Dirichlet Allocation (LDA) model, and show how k-means as well as non-negative matrix factorization (NMF) can also be interpreted as topic
■520 ▼aIn Chapter 2 we present two algorithms for the data-distributed non-negative matrix factorization (NMF) task, and one for the singular value decomposition (SVD). In the offline setting, M parties have already computed NMF models of their local d
■520 ▼aIn Chapter 3 we study empirical measures of Distributional Differential Privacy. We want to measure to what extent one participant in a distributed computation can correctly identify the presence of a single document in another participant's dat
■590 ▼aSchool code: 0185.
■650 4▼aComputer science
■690 ▼a0984
■71020▼aRensselaer Polytechnic Institute▼bComputer Science.
■7730 ▼tDissertation Abstracts International▼g79-12B(E).
■773 ▼tDissertation Abstract International
■790 ▼a0185
■791 ▼aPh.D.
■792 ▼a2018
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T14997192▼nKERIS
■980 ▼a201812▼f2019
