ADMM for least square problems with pairwise-difference penalties for coefficient grouping
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박수희 | - |
dc.contributor.author | 신승준 | - |
dc.date.accessioned | 2022-08-27T00:40:18Z | - |
dc.date.available | 2022-08-27T00:40:18Z | - |
dc.date.created | 2022-08-26 | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/143527 | - |
dc.description.abstract | In the era of bigdata, scalability is a crucial issue in learning models. Among many others, the Alternating Direction of Multipliers (ADMM, Boyd it et al., 2011) algorithm has gained great popularity in solving large-scale problems efficiently. In this article, we propose applying the ADMM algorithm to solve the least square problem penalized by the pairwise-difference penalty, frequently used to identify group structures among coefficients. ADMM algorithm enables us to solve the high-dimensional problem efficiently in a unified fashion and thus allows us to employ several different types of penalty functions such as LASSO, Elastic Net, SCAD, and MCP for the penalized problem. Additionally, the ADMM algorithm naturally extends the algorithm to distributed computation and real-time updates, both desirable when dealing with large amounts of data. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 한국통계학회 | - |
dc.title | ADMM for least square problems with pairwise-difference penalties for coefficient grouping | - |
dc.title.alternative | ADMM for least square problems with pairwise-difference penalties for coefficient grouping | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 신승준 | - |
dc.identifier.doi | 10.29220/CSAM.2022.29.4.441 | - |
dc.identifier.scopusid | 2-s2.0-85135815973 | - |
dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.29, no.4, pp.441 - 451 | - |
dc.relation.isPartOf | Communications for Statistical Applications and Methods | - |
dc.citation.title | Communications for Statistical Applications and Methods | - |
dc.citation.volume | 29 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 441 | - |
dc.citation.endPage | 451 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002864982 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.description.journalRegisteredClass | other | - |
dc.subject.keywordAuthor | alternating direction of multipliers | - |
dc.subject.keywordAuthor | grouping coefficients | - |
dc.subject.keywordAuthor | real-time update | - |
dc.subject.keywordAuthor | high-dimensional data | - |
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