Dear Michael, Thanks for your reply. In my case, the original dimension of the coefficient matrix is very large, including ~10,000 elements, but actually there are only several hundred of independent elements in the coefficient matrix based on the some symmetric nature of my data.
I know how to build the coefficient matrix with independent elements and do the ordinary least-square fitting. However, an over-fitting issue may arise unless the number of individual reference data is fairly large compared with the number of parameters. So I am wondering if there is a way to use LASSO method to deal with this problem. And I think the efficiency would also increase if we can define a constrained coefficient matrix (only constructed from some independent elements) for LASSO method. But it seem to be not possible to define such a constrained coefficient matrix in "sklearn". Am I right? Best Guoqiang ________________________________ From: Guoqiang Lan, Mr Sent: January 2, 2016 4:19 PM To: scikit-learn-general@lists.sourceforge.net Subject: LASSO, Constrained coefficient matrix with some independent elements 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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