Jason Rennie wrote: > Note that EM can be very slow to converge: > > http://www.cs.toronto.edu/~roweis/papers/emecgicml03.pdf > <http://www.cs.toronto.edu/%7Eroweis/papers/emecgicml03.pdf> > > EM is great for churning-out papers, not so great for getting real > work done.
I think it depends on what you are doing - EM is used for 'real' work too, after all :) > Conjugate gradient is a much better tool, at least in my (and > Salakhutdinov's) experience. Thanks for the link, I was not aware of this work. What is the difference between the ECG method and the method proposed by Lange in [1] ? To avoid 'local trapping' of the parameter in EM methods, recursive EM [2] may also be a promising method, also it seems to me that it has not been used so much, but I may well be wrong (I have seen several people using a simplified version of it without much theoretical consideration in speech processing). cheers, David [1] "A gradient algorithm locally equivalent to the EM algorithm", in Journal of the Royal Statistical Society. Series B. Methodological, 1995, vol. 57, n^o 2, pp. 425-437 [2] "Online EM Algorithm for Latent Data Models", by: Olivier Cappe;, Eric Moulines, in the Journal of the Royal Statistical Society Series B (February 2009). _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
