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Regularization path for linear model via net method
Date Issued
2012
ISBN
978-364231019-5
ISSN
16113349
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7332 LNCS(PART 2), pp. 414-421
Type
Conference Paper
Abstract
We investigate a net regularization method for variable selection in the linear model, which has convex loss function and concave penalty. Meanwhile, the net regularization based on the use of the Lr penalty with 1/2 ≤ r ≤ 1. In the simulation we will demonstrate that the net regularization is more efficient and more accurate for variable selection than Lasso. © 2012 Springer-Verlag.
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