期刊
AUTOMATICA
卷 48, 期 9, 页码 2107-2115出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2012.06.051
关键词
Model selection; RIVAL; Positive Lasso; Nuclear material detection application
资金
- DoE [DE-FG52-09NA29364]
In many situations, the number of data points is fixed, and the asymptotic convergence results of popular model selection tools may not be useful. A new algorithm for model selection, RIVAL (removing irrelevant variables amidst Lasso iterations), is presented and shown to be particularly effective for a large but fixed number of data points. The algorithm is motivated by an application of nuclear material detection where all unknown parameters are to be non-negative. Thus, positive Lasso and its variants are analyzed. Then, RIVAL is proposed and is shown to have some desirable properties, namely the number of data points needed to have convergence is smaller than existing methods. (C) 2012 Elsevier Ltd. All rights reserved.
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