A fast algorithm for non-negativity model selection

An efficient optimization algorithm for identifying the best least squares regression model under the condition of non-negative coefficients is proposed. The algorithm exposits an innovative solution via the unrestricted least squares and is based on the regression tree and branch-and-bound techniqu...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Statistics and computing Jg. 23; H. 3; S. 403 - 411
Hauptverfasser: Gatu, Cristian, Kontoghiorghes, Erricos John
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.05.2013
Schlagworte:
Algorithms
Artificial Intelligence
Computation
Effectiveness
Least squares method
Mathematical models
Mathematics and Statistics
Probability and Statistics in Computer Science
Regression
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Strategy
Non-negative least squares
Subset selection
Branch-and-bound algorithms
ISSN:0960-3174, 1573-1375
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:An efficient optimization algorithm for identifying the best least squares regression model under the condition of non-negative coefficients is proposed. The algorithm exposits an innovative solution via the unrestricted least squares and is based on the regression tree and branch-and-bound techniques for computing the best subset regression. The aim is to filling a gap in computationally tractable solutions to the non-negative least squares problem and model selection. The proposed method is illustrated with a real dataset. Experimental results on real and artificial random datasets confirm the computational efficacy of the new strategy and demonstrates its ability to solve large model selection problems that are subject to non-negativity constrains.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-012-9318-8