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Keep in mind that any similarity measure is mapping a high dimensional space onto a one dimensional space. The volume of vectors within a cosine of another vect
by 0xfaded 8y ago
Keep in mind that any similarity measure is mapping a high dimensional space onto a one dimensional space. The volume of vectors within a cosine of another vector is an N dimensional cone with volume rapidly decaying towards 0 for N > 5 (assuming positive cosine). Therefore cosine is not a particularly good metric in high dimensional spaces.
- ivalm 8y agoKind of, w2v is cosine similarity driven and there embeddings often go up to 300 dimensions.
- SubiculumCode 8y agoFor n ~ 250k to 1.5M, I've use Manhattan distance (as opposed to Euclidean) in an analysis of neuroimaging functional data...but I am interested in people's takes on choosing a distance metric..There are so many exotics out there.
- nixpulvis 8y agoWhat if my model is really good and maps to the angle between two vectors almost perfectly?