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Activity Number:
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194
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #302341 |
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Title:
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Adaptive Regularization Through Entire Solution Surface
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Author(s):
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Seongho Wu*+
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Companies:
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The University of Minnesota
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Address:
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313 Ford Hall, 224 Church St. S.E., Minneapolis, MN, 55455,
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Keywords:
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Homotopy ; Least squares ; Subdifferential ; Support vector machine ; Variable selcetion ; Variable grouping
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Abstract:
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Several sparseness penalties have been suggested for delivery of good predictive performance in automatic variable selection within the framework of regularization. All assume that the true model is sparse. We propose a penalty, a convex combination of the L1- and Linf-norms, that adapts to a variety of situations including sparseness and nonsparseness, grouping and nongrouping. The proposed penalty performs grouping and adaptive regularization. In addition, we introduce a novel homotopy algorithm utilizing subgradients for developing regularization solution surfaces involving multiple regularizers. This permits efficient computation and adaptive tuning. Numerical experiments are conducted via simulation. In simulated and real examples, the proposed penalty compares well against popular alternatives.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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