{"data":{"id":"10.6084/m9.figshare.1378871","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1378871","prefix":"10.6084","suffix":"m9.figshare.1378871","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Donghyeon Yu","affiliation":[],"nameIdentifiers":[]},{"name":"Joong-Ho Won","affiliation":[],"nameIdentifiers":[]},{"name":"Taehoon Lee","affiliation":[],"nameIdentifiers":[]},{"name":"Lim, Johan","nameType":"Personal","givenName":"Johan","familyName":"Lim","affiliation":[],"nameIdentifiers":[]},{"name":"Sungroh Yoon","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"High-Dimensional Fused Lasso Regression Using Majorization–Minimization and Parallel Processing"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Science Policy"},{"subject":"Mathematics"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Information and Computing Sciences"},{"subject":"Biological Sciences"},{"subject":"Chemistry"},{"subject":"Earth and Environmental Sciences"},{"subject":"Engineering"}],"contributors":[],"dates":[{"date":"2015-04-14","dateType":"Created"},{"date":"2015-12-03","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/10618600.2013.878662","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2028830 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"In this article, we propose a majorization–minimization (MM) algorithm for high-dimensional fused lasso regression (FLR) suitable for parallelization using graphics processing units (GPUs). The MM algorithm is stable and flexible as it can solve the FLR problems with various types of design matrices and penalty structures within a few tens of iterations. We also show that the convergence of the proposed algorithm is guaranteed. We conduct numerical studies to compare our algorithm with other existing algorithms, demonstrating that the proposed MM algorithm is competitive in many settings including the two-dimensional FLR with arbitrary design matrices. The merit of GPU parallelization is also exhibited. Supplementary materials are available online.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/articles/dataset/High_Dimensional_Fused_Lasso_Regression_Using_Majorization_8211_Minimization_and_Parallel_Processing/1378871","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2015-04-14T13:53:10.000Z","registered":"2015-04-14T13:53:11.000Z","published":"2015","updated":"2020-09-04T18:06:28.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.1378871","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}