Hi Luca, The other part of the decomposition that you're missing is available in `spca.components_` and has shape `(n_components, n_features)`. The approximation of X is therefore `np.dot(x_3_dimensional, spca.components_)`.
Best, Vlad On Thu, Oct 16, 2014 at 6:07 PM, Luca Puggini <lucapug...@gmail.com> wrote: > Hi, > is there any way to reconstruct the data after SparsePCA? > > If I do > > spca = SparsePCA(alpha=1, n_components=3).fit(x) > x_3_dimensional = SparsePCA.transform(x) > > How can I get the best lower rank approximation of x after SparsePCA > decomposition? > > Thanks, > Luca > > ------------------------------------------------------------------------------ > Comprehensive Server Monitoring with Site24x7. > Monitor 10 servers for $9/Month. > Get alerted through email, SMS, voice calls or mobile push notifications. > Take corrective actions from your mobile device. > http://p.sf.net/sfu/Zoho > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ Comprehensive Server Monitoring with Site24x7. Monitor 10 servers for $9/Month. Get alerted through email, SMS, voice calls or mobile push notifications. Take corrective actions from your mobile device. http://p.sf.net/sfu/Zoho _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
