Hi there! Currently, the Ledoit Wolf covariance estimation does not standardize the data before computing the shrinkage parameter. However, I think this is not correct, because the data should have zero mean and unit L2 norm for the algorithm to work correctly.
I only found one reference which explicitly states this (page 241, right after Eq. 2): http://ba.stat.cmu.edu/journal/2010/vol05/issue02/gramacy.pdf I implemented a real-world test using shrinkage LDA. On my data set, I get 100% accuracy when using the empirical covariance. I also get 100% when I use the correctly shrunk cov (standardizing the data). However, I only get around 50% when using the current sklearn implementation. I have also posted an issue on GitHub: https://github.com/scikit-learn/scikit-learn/issues/3508 Should I create a PR to fix this, or am I completely missing something? Clemens ------------------------------------------------------------------------------ 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
