Hi
I would like to solve the standard Lasso optimization:
(1 / (2 * n_samples)) * ||Y - XW||^2_2 + alpha * ||W||_1
Only difference being that I want to take Y to be a matrix. This would
mean that W is also a matrix. Is it a good idea to use the lasso module
written in scikit-learn?
I have tried it on a number of matrices generated randomly, the
optimization forces entire columns to go to zero. The resulting matrix W
will have many zero columns.
Thanks.
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sp
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