Short note and pointer to Julia implementation of the (very recently proposed and beautifully simple) EM algorithm for regularized L0 regression (L0EM):
https://github.com/robertfeldt/FeldtLib.jl/blob/master/spikes/L0EM_regularized_regression.jl that someone might find useful if doing regularized regression. I also experimented with an adaptive lambda (regularization weight) binary search scheme which can find interesting lambda values without having to run the normal set of 100 different values. It seems useful and I will extend it with log-spaced binary search in the near future. Worth noting is that the paper on L0EM found that just using AIC or BIC to select lambda was very efficient => cross validation might not be needed => 500-1000 times speedup => useful on "big data"... I have several other regularized regression methods implemented in Julia so maybe time to collect them together into a lib. Anyone knows if there is something already out there (I have seen GLMNET.jl which wraps the fortran LASSO code)? Cheers, Robert Feldt
