David Warde-Farley wrote: > On 9-Jun-09, at 3:54 AM, David Cournapeau wrote: > > >> For example, what ML people call PCA is called Karhunen Loéve in >> signal >> processing, and the concepts are quite similar. >> > > > Yup. This seems to be a nice set of review notes: > > http://www.ece.rutgers.edu/~orfanidi/ece525/svd.pdf >
This looks indeed like a very nice review from a signal processing approach. I never took the time to understand the similarities/differences/connections between traditional SP approaches and the machine learning approach. I wonder if the subspaces methods ala PENCIL/MUSIC and co have a (useful) interpretation in a more ML approach, I never really thought about it. I guess other people had :) > And going further than just PCA/KLT, tying it together with maximum > likelihood factor analysis/linear dynamical systems/hidden Markov > models, > > http://www.cs.toronto.edu/~roweis/papers/NC110201.pdf > As much as I like this paper, I always felt that you miss a lot of insights when considering PCA only from a purely statistical POV. I really like the consideration of PCA within a function approximation POV (the chapter 9 of the Mallat book on wavelet is cristal clear, for example, and it is based on all those cool functional spaces theory likes Besov space). cheers, David _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
