-norm penalised orthogonal forward regression

Xia Hong, Sheng Chen, Yi Guo and Junbin Gao

International Journal of Systems Science, 2017, vol. 48, issue 10, 2195-2201

Abstract: A l1-norm penalised orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leave-one-out mean square error (LOOMSE), by defining a new l1-norm penalised cost function in the constructed orthogonal space and associating each orthogonal basis with an individually tunable regularisation parameter. Due to orthogonality, the LOOMSE can be analytically computed without actually splitting the data-set, and moreover a closed form of the optimal regularisation parameter is derived by greedily minimising the LOOMSE incrementally. We also propose a simple formula for adaptively detecting and removing regressors to an inactive set so that the computational cost of the algorithm is significantly reduced. Examples are included to demonstrate the effectiveness of this new l1-POFR approach.

Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2017.1311383 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:48:y:2017:i:10:p:2195-2201

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2017.1311383

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().