On Thu, Nov 3, 2011 at 6:28 AM, David Warde-Farley <[email protected]> wrote:
> I wonder how this compares to learning a linear tied-weights autoencoder > with SGD and then just orthogonalizing the weight vectors (I suppose you'd > also need to do one run with a single "neuron" in order to orient the basis > with respect to the first p.c.). I was thinking of something similar: just a least-squares objective minimized by SGD. Would be nice to compare with RandomizedPCA both in terms of training time and performance on the final supervised objective. By the way, how would go about the orthogonalization? Gram–Schmidt? Mathieu ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
