> The algorithm proposed in this paper, is rather similar to that of the Lars > LASSO, but with a complicating > factor being a non-negative constraint on the shrinkage factor. (See eq. (2) > in this paper) > Once you've computed your shrinkage factor, you basically have your > regression coefficients > seeing as your NG coefficient = shrinkage factor * regression coefficient
that's exactly what I implemented. > He showed it to be a stable selection method and often outperforms it's > competitors like > subset regression and ridge regression. > The solution path of the NG is piece-wise linear and it's whole path can be > computed quickly. we could indeed have a path easily using LassoLars > It is also path-consistent (A solution that contains at least one desirable > estimate) given an appropriate initial estimate. The path-consistency of the > NG is highlighted to be in contrast to the fact that the LASSO is not always > path consistent (Peng Zhao & Hui Zou, personal communication). It is argued > that the NG has the ability to turn > a consistent estimate into an estimate that is both consistent in terms of > estimation and in terms of variable selection. hum. If it can be consistent on support and coef amplitudes that's neat. > A drawback is the NG's explicit reliance on the full least square estimate, > as a small sample size may cause it to perform poorly - however a ridge > regression is suggested as an initial estimate for defining the NG estimate, > instead > of the least square estimate. good to know it seems to me that it might be a good addition to the scikit if can convince ourselves with examples that it does better than a Lasso. Alex ------------------------------------------------------------------------------ Better than sec? Nothing is better than sec when it comes to monitoring Big Data applications. Try Boundary one-second resolution app monitoring today. Free. http://p.sf.net/sfu/Boundary-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
