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020 ▼a 9780438206403
035 ▼a (MiAaPQ)AAI10751762
035 ▼a (MiAaPQ)rpi:11252
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Tsikhanovich, Maksim.
24510 ▼a Unsupervised Learning: Evaluation, Distributed Setting, and Privacy.
260 ▼a [S.l.] : ▼b Rensselaer Polytechnic Institute., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 134 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Malik Magdon-Ismail.
5021 ▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
520 ▼a Chapter 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 ▼a In 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 ▼a In 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 ▼a School code: 0185.
650 4 ▼a Computer science.
690 ▼a 0984
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=T14997192 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자