4 ms·
What is the cheapest way to capture similarity if not via dot product then?
by soarerz 3y ago
What is the cheapest way to capture similarity if not via dot product then?
- deleted 3y ago[deleted]
- gajus 3y agoInterested to know as well
- latency-guy2 3y agoI don't have an answer for this really outside of silly ones like "strict equality check", but I assert that no one else does either, at least today and right now, and its an inherent limitation due to the nature of embeddings and the space it desires to be (cheap, fast, good enough similarity for your use case). You're probably best off using the commercial suggestion, and if its dot product, go for it. I am no expert in this area and my interest wanes every day.
- scotty79 3y agoInstead of sums of multiplications you could for example use sum of squares of differences. Means squared error instead of dot product, it's not cheaper but it's close If you want to go cheaper you could use sum of abs of differences.
- soarerz 3y agoThis is effectively "the same" as dot product. For a lot of embeddings we have today, norm of any embedding vector is roughly of same size, so the angle between two vectors is roughly same size as length of difference that you are saying, and can be expressed in terms of 1 - dot product after scaling