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 \(\frac{1}{2}\leq\) r ≤1. In the simulation we will demonstrate that the net regularization is more efficient and more accurate for variable selection than Lasso.
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Luan, XZ. et al. (2012). Regularization Path for Linear Model via Net Method. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_49
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DOI: https://doi.org/10.1007/978-3-642-31020-1_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31019-5
Online ISBN: 978-3-642-31020-1
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