File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/s10915-011-9507-1
- Scopus: eid_2-s2.0-84864128199
- WOS: WOS:000302259900001
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Alternating direction method for covariance selection models
Title | Alternating direction method for covariance selection models |
---|---|
Authors | |
Keywords | Log-likelihood Covariance selection problem Alternating direction method |
Issue Date | 2012 |
Citation | Journal of Scientific Computing, 2012, v. 51, n. 2, p. 261-273 How to Cite? |
Abstract | The covariance selection problem captures many applications in various fields, and it has been well studied in the literature. Recently, an l 1 -norm penalized log-likelihood model has been developed for the covariance selection problem, and this novel model is capable of completing the model selection and parameter estimation simultaneously. With the rapidly increasing magnitude of data, it is urged to consider efficient numerical algorithms for large-scale cases of the l 1 -norm penalized log-likelihood model. For this purpose, this paper develops the alternating direction method (ADM). Some preliminary numerical results show that the ADM approach is very efficient for large-scale cases of the l 1 -norm penalized log-likelihood model. © Springer Science+Business Media, LLC 2011. © Springer Science+Business Media, LLC 2011. |
Persistent Identifier | http://hdl.handle.net/10722/250997 |
ISSN | 2023 Impact Factor: 2.8 2023 SCImago Journal Rankings: 1.248 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yuan, Xiaoming | - |
dc.date.accessioned | 2018-02-01T01:54:17Z | - |
dc.date.available | 2018-02-01T01:54:17Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | Journal of Scientific Computing, 2012, v. 51, n. 2, p. 261-273 | - |
dc.identifier.issn | 0885-7474 | - |
dc.identifier.uri | http://hdl.handle.net/10722/250997 | - |
dc.description.abstract | The covariance selection problem captures many applications in various fields, and it has been well studied in the literature. Recently, an l 1 -norm penalized log-likelihood model has been developed for the covariance selection problem, and this novel model is capable of completing the model selection and parameter estimation simultaneously. With the rapidly increasing magnitude of data, it is urged to consider efficient numerical algorithms for large-scale cases of the l 1 -norm penalized log-likelihood model. For this purpose, this paper develops the alternating direction method (ADM). Some preliminary numerical results show that the ADM approach is very efficient for large-scale cases of the l 1 -norm penalized log-likelihood model. © Springer Science+Business Media, LLC 2011. © Springer Science+Business Media, LLC 2011. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Scientific Computing | - |
dc.subject | Log-likelihood | - |
dc.subject | Covariance selection problem | - |
dc.subject | Alternating direction method | - |
dc.title | Alternating direction method for covariance selection models | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s10915-011-9507-1 | - |
dc.identifier.scopus | eid_2-s2.0-84864128199 | - |
dc.identifier.volume | 51 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 261 | - |
dc.identifier.epage | 273 | - |
dc.identifier.isi | WOS:000302259900001 | - |
dc.identifier.issnl | 0885-7474 | - |