Github user dbtsai commented on the pull request:
https://github.com/apache/spark/pull/1379#issuecomment-63904113
@avulanov I will merge this on Spark 1.3, and sorry for delay since I was
very busy recently. Yes, the branch you found should work, but it can not be
cleanly merged in upstream, and I'm working on it. You can try that branch for
now. Also, in the branch, we don't use LBFGS as optimizer, so the convergent
rate will be slow.
Basically, you can model the whole problem using (num_features +
1)(num_classes), but the solution will not be unique. You can chose one of the
class as base class to make the solution unique, and I chose the first class as
base class. See `Properties of softmax regression parameterization` in the wiki
page you refer. Or my presentation
http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297 for more technical
detail. You can think about binary logistic regression, and you only have
(num_features + 1) coefficients instead of 2 * (num_features + 1)
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