Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
696914 | Automatica | 2012 | 9 Pages |
Abstract
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.
Keywords
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Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Paul Kump, Er-Wei Bai, Kung-sik Chan, Bill Eichinger, Kang Li,