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when searching the KNN point with approximation, why Scanorama use "manhattan" metric? In my opinion, it should use "cosine" distance without L2 Normalization or "euclidean" distance with L2 Normalization, it is very strange to use "manhattan" distance to find KNN? Cound you give some interpretation about this problem? Thank you!
The text was updated successfully, but these errors were encountered:
when searching the KNN point with approximation, why Scanorama use "manhattan" metric? In my opinion, it should use "cosine" distance without L2 Normalization or "euclidean" distance with L2 Normalization, it is very strange to use "manhattan" distance to find KNN? Cound you give some interpretation about this problem? Thank you!
The text was updated successfully, but these errors were encountered: