On Tue, Dec 13, 2011 at 07:31:36PM +0530, Jaidev Deshpande wrote: > >> x_cent=zscore(x) % normalizing the data
Zscore centers and scales, right? > >> [v,d]=eig(cov(x_cent)) > >> v=fliplr(v) % the eigenvectors need to be flipped because MATLAB returns > >> them in the descending order > >> x_red = x_cent' * v(:,1:2) > With decomposition.PCA I did the following (it's almost like the PCA > vs LDA example in the docs): > >>> pca=PCA(n_components=2) > >>> x_red=pca.fit(x).transform(x) > What's so different about the PCA implementation here in sklearn that > I'm getting much more streamlined points in the scatter-plot? PCA doesn't scale the input data. G ------------------------------------------------------------------------------ Cloud Computing - Latest Buzzword or a Glimpse of the Future? This paper surveys cloud computing today: What are the benefits? Why are businesses embracing it? What are its payoffs and pitfalls? http://www.accelacomm.com/jaw/sdnl/114/51425149/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
