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More stable and efficient Mahalanobis distance #40
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For SpectralSignatures we might want to consider |
Thanks for this! Given that this doesn't seem to be a bottleneck for now, probably shouldn't spend too much time on it, but I'm getting slightly nerdsniped. If we assume the covariance matrix is full rank, then I think torch.cholesky_solve is probably much better than I think allowing non-full rank covariance matrices (typically from fewer samples than dimensions of the activation) is kind of nice, but I'm not sure anyway whether using a pseudoinverse/ If we want to keep the current behavior, we could keep track how many samples were used to compute the covariance matrix, and then use cholesky iff |
Oh, this is of course even better than |
Oh, that question title is spot on. Shows how much research I've done... ^^' |
Note that |
Possible compromise between simplicity and speed/stability: use the cholesky method from above by default, but have a fallback using pinv if the user passes in |
Sounds like a good compromise. |
We currently use
pinv
, should probably use https://pytorch.org/docs/stable/generated/torch.linalg.lstsq.html instead (or maybe some third thing). Doesn't seem like a big issue so far thoughThe text was updated successfully, but these errors were encountered: