We have updated our paper on optimally weighted heteroscedastic PCA and here is the code to run the experiments. In this work we show how to weight data before solving PCA, under a spiked covariance model with heteroscedastic additive noise. Surprisingly, the weights are not inverse noise variance, and neither are they 0/1 discarding the noisiest points, but instead the optimal weights are in between these two standard heuristics.
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