Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/1290#issuecomment-62191470
f is neural activation...it can be tanh or sigmoid function (they are
non-convex, nonlinear) , LRU units (max is convex)...in this PR
https://github.com/apache/spark/pull/2705 I am experimenting with convex and
nonlinear functions for matrix factorization loss..Idea is to use the gradient
interfaces for the loss functions...if f(H1'X) can break component wise we can
re-use lot of ALS development...
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