Dear Guoqiang, it sounds as though you could just throw all the irrelevant variables away and then do an ordinary least squares or ridge regression on what you keep. That is if I understand correctly that you have already successfully identified the support. If this is not the case, could you try re-explaining, detailing exactly the nature of the information you have given for your problem?
Michael On Saturday, January 2, 2016, Guoqiang Lan, Mr <guoqiang....@mail.mcgill.ca> wrote: > Dear all, > > I am using the LASSO model to optimize a huge sparse coefficient-matrix, > W. Luckily, I have known how many independent elements and how they > distribute in the coefficient matrix. What I want to obtain now is just the > values of these independent elements. Is there a way to define such a > constrained coefficient matrix (only constructed from some independent > elements) and use it to do the optimization with LASSO method in 'sklearn'? > Or is there any suggestion to figure out this problem? > > Happy new years. > > Best > > Guoqiang >
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