Hi DB Tsai,
Firstly I must show my deep appreciation towards your kind help.
Did you just mean like this, currently there is no way for users to deal with
constrains like all weights >= 0 in spark, though spark also has LBFGS ...
Moreover, I did not know whether spark SVD will help some for that
Dear All,
As for N dimension linear regression, while the labeled training points number
(or the rank of the labeled point space) is less than N, then from perspective
of math, the weight of the trained linear model may be not unique.
However, the output of model.weight() by spark may be with
For the constrains like all weights >=0, people do LBFGS-B which is
supported in our optimization library, Breeze.
https://github.com/scalanlp/breeze/issues/323
However, in Spark's LiR, our implementation doesn't have constrain
implementation. I do see this is useful given we're experimenting
