What ended up launching is a fancy product quantization based on k-means. Some of the tricks were storing magnitude separately (i.e. removing the mean) and rear

What ended up launching is a fancy product quantization based on k-means. Some of the tricks were storing magnitude separately (i.e. removing the mean) and rearranging dimensions based on variance (and/or rotation based on PCA) for PQ to work better. I also remember trying to fit a distribution so that I can generate synthetic data (not for a lack of data, but more for understanding the problem space better). The synthetic data quantized pretty differently - my guess is that it's because of random areas of density and sparsity. I'm not quite following your exact rearranging idea though. Not sure if the above answers the question.