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Activity Number:
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326
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 8, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #307537 |
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Title:
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Determination of Regularization Parameter Using L-Curve by LARS-LASSO Algorithm
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Author(s):
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Leming Qu*+ and Partha Routh
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Companies:
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Boise State University and Boise State University
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Address:
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1910 University Drive, Boise, ID, 83725,
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Keywords:
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l-curve ; regularization ; ill-posed problems ; LARS ; LASSO
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Abstract:
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Regularization is a common technique to obtain reasonable solutions to ill-posed problems. In Tikhonov regularization, both the data-fitting and the penalty terms are in L2 norm. The L-curve is a plot of the size of the regularized solution versus the size of the corresponding residual for all valid regularization parameters. It is a useful tool for determining a suitable value of the regularization parameter. LASSO replaces the L2 norm by L1 norm for the penality term. The LARS algorithm computes the whole path of the LASSO with a computational complexity in the same magnitude as the ordinary least squares. Thus, the L-curve for LASSO can be obtained efficiently by the LARS-LASSO algorithm. The tuning point of the L-curve is chosen as the value of the regularization parameter. We compare L-curve method with existing methods, including GCV and C_p.
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