DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yong-Deok Kim | - |
dc.contributor.author | Choi, S | - |
dc.date.accessioned | 2016-03-31T08:31:00Z | - |
dc.date.available | 2016-03-31T08:31:00Z | - |
dc.date.created | 2013-07-01 | - |
dc.date.issued | 2013-03 | - |
dc.identifier.issn | 1070-9908 | - |
dc.identifier.other | 2013-OAK-0000027721 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/15481 | - |
dc.description.abstract | Matrix factorization with trace norm regularization is a popular approach to matrix completion and collaborative filtering. When entries of the matrix are sampled non-uniformly (which is the case for collaborative prediction), a properly weighted correction to the trace norm regularization is known to improve the performance dramatically. While the weighted trace norm regularization has been rigorously studied, its generative counterpart is not known yet. In this paper we show that the weighted trace norm regularization emerges from variational Bayesian matrix factorization where variational distributions over factor matrices are restricted to be isotropic Gaussians with the common variance. We show that variational variance corresponds to the regularization parameter. Thus, the regularization parameter can be automatically learned by variational inference rather than cross-validation. Experiments on MovieLens and Netflix datasets confirm the variational Bayesian perspective of the weighted trace norm regularization, demonstrating that variational parameter learned by variational inference agrees with the value of the regularization parameter found by cross-validation. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE Signal Processing Letters | - |
dc.subject | Collaborative prediction | - |
dc.subject | matrix completion | - |
dc.subject | matrix factorization | - |
dc.subject | trace norm regularization | - |
dc.subject | variational Bayesian inference | - |
dc.subject | RECOMMENDER SYSTEMS | - |
dc.subject | COMPLETION | - |
dc.title | Variational Bayesian view of weighted trace norm regularization for matrix factorization, | - |
dc.type | Article | - |
dc.contributor.college | 정보전자융합공학부 | - |
dc.identifier.doi | 10.1109/LSP.2013.2242468 | - |
dc.author.google | Kim, YD | - |
dc.author.google | Choi, S | - |
dc.relation.volume | 20 | - |
dc.relation.issue | 3 | - |
dc.relation.startpage | 261 | - |
dc.relation.lastpage | 264 | - |
dc.contributor.id | 10077620 | - |
dc.relation.journal | IEEE Signal Processing Letters | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE Signal Processing Letters, v.20, no.3, pp.261 - 264 | - |
dc.identifier.wosid | 000314828600004 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 264 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 261 | - |
dc.citation.title | IEEE Signal Processing Letters | - |
dc.citation.volume | 20 | - |
dc.contributor.affiliatedAuthor | Choi, S | - |
dc.identifier.scopusid | 2-s2.0-84873675681 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 2 | - |
dc.description.scptc | 5 | * |
dc.date.scptcdate | 2018-05-121 | * |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Collaborative prediction | - |
dc.subject.keywordAuthor | matrix completion | - |
dc.subject.keywordAuthor | matrix factorization | - |
dc.subject.keywordAuthor | trace norm regularization | - |
dc.subject.keywordAuthor | variational Bayesian inference | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
library@postech.ac.kr Tel: 054-279-2548
Copyrights © by 2017 Pohang University of Science ad Technology All right reserved.