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dc.contributor.authorDongha, Lee-
dc.contributor.authorJinoh, Oh-
dc.contributor.authorHwanjo, Yu-
dc.date.available2019-12-06T06:50:03Z-
dc.date.created2019-12-04-
dc.date.issued2020-04-
dc.identifier.issn0020-0255-
dc.identifier.urihttp://oasis.postech.ac.kr/handle/2014.oak/100442-
dc.description.abstractRecently, there are increasing reports that most datasets can be actually stored in disks of a single off-the-shelf workstation, and utilizing out-of-core methods is much cheaper and even faster than using a distributed system. For these reasons, out-of-core methods have been actively developed for machine learning and graph processing. The goal of this paper is to develop an efficient out-of-core matrix completion method based on coordinate descent approach. Coordinate descent-based matrix completion (CD-MC) has two strong benefits over other approaches: 1) it does not involve heavy computation such as matrix inversion and 2) it does not have step-size hyper-parameters, which reduces the effort for hyper-parameter tuning. Existing solutions for CD-MC have been developed and analyzed for in-memory setting and they do not take disk-I/O into account. Thus, we propose OCam, a novel out-of-core coordinate descent algorithm for matrix completion. Our evaluation results and cost analyses provide sound evidences supporting the following benefits of OCam: (1) Scalability – OCam is a truly scalable out-of-core method and thus decomposes a matrix larger than the size of memory, (2) Efficiency – OCam is super fast. OCam is up to 10x faster than the state-of-the-art out-of-core method, and up to 4.1x faster than a competing distributed method when using eight machines. The source code of OCam will be available for reproducibility.-
dc.languageEnglish-
dc.publisherElsevier-
dc.titleOCAM: Out-of-core Coordinate Descent Algorithm for Matrix Completion-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.bibliographicCitationInformation Sciences, v.514-
dc.identifier.wosid미등재-
dc.citation.titleInformation Sciences-
dc.citation.volume514-
dc.contributor.affiliatedAuthorDongha, Lee-
dc.contributor.affiliatedAuthorHwanjo, Yu-
dc.identifier.scopusid2-s2.0-85075538204-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.type.docTypeArticle-

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