2012/12/14 Gael Varoquaux <[email protected]>: > On Fri, Dec 14, 2012 at 05:51:08PM +0100, federico vaggi wrote: >> In this case, X is: > >> (n_samples, n_features, M_bootstrapped_iterations): > >> So I thought I could get a better estimate by taking each M-length vector and >> calculating the covariance against every other vector, which would result in >> a >> (n_samples * n_features, n_samples* n_features) sized matrix, not a >> (n_samples, >> n_features) sized matrix. > > Compute an (n_features, n_features) covariance matrix for each > bootsrapped iteration and average them.
Also if the individual covariance matrix estimates are sparse, it might be worth do a majority vote over the bootstrapped variants to find a reasonably robust yet sparse support otherwise the averaging itself might break the sparsity. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ LogMeIn Rescue: Anywhere, Anytime Remote support for IT. Free Trial Remotely access PCs and mobile devices and provide instant support Improve your efficiency, and focus on delivering more value-add services Discover what IT Professionals Know. Rescue delivers http://p.sf.net/sfu/logmein_12329d2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
