Hi, I used scikit-learn's LDA for dimensionality reduction and noticed that the projected linear discriminants look a little bit strange. For testing, I then used the Iris dataset and could reproduce the results that are posted on scikit-learn's example documentation here: http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html
However, when I implemented the LDA in a step by-step approach, the results look quite different and more like somehing I would expect. I tried 3 different approaches 1) on the raw data 2) on z-score standardized data (although all units are centimeter in the Iris dataset) 3) on mean-centered data The results from 2) and 3) are similar to what R's LDA function produces as a hint that there might be no error in my step-by-step implementation. The code that I used for the comparison can be found here: http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html It would be great if someone could have a look at this issue. I posted it as question to StackOverflow with images to explain the observed differences: http://stackoverflow.com/questions/25003427/resulting-projection-from-scikit-learns-lda-is-quite-different-from-lda-in-r-or ------------------------------------------------------------------------------ Infragistics Professional Build stunning WinForms apps today! Reboot your WinForms applications with our WinForms controls. Build a bridge from your legacy apps to the future. http://pubads.g.doubleclick.net/gampad/clk?id=153845071&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
