shrinkage Spark2.0机器学习系列之12： 线性回归及L1、L2正(3)

The L1-regularized formulation is useful in some contexts due to its tendency to prefer solutions where more parameters are zero, which gives solutions that depend on fewer variables.[8] For this reason, the Lasso and its variants are fundamental to the field of compressed sensing. An extension of this approach is elastic net regularization.（）

杨灿写的《统计学习那些事》一文， 一定要看看，文中对许多方法的内在联系有一个概要的阐述：

”Tibshrani自己说他的Lasso是受到Breiman的Non-NegativeGarrote(NNG)的启发。Lasso把NNG的两步合并为一步，即L1-normregularization。Lasso的巨大优势在于它所构造的模型是Sparse的，因为它会自动地选择很少一部分变量构造模型。”

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