About curse of dimensionality

Posted by Dan on Stack Overflow See other posts from Stack Overflow or by Dan
Published on 2010-05-16T12:26:44Z Indexed on 2010/05/16 12:30 UTC
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database

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dimensions

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similarity

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image-processing

My question is about this topic I've been reading about a bit. Basically my understanding is that in higher dimensions all points end up being very close to each other.

The doubt I have is whether this means that calculating distances the usual way (euclidean for instance) is valid or not. If it were still valid, this would mean that when comparing vectors in high dimensions, the two most similar wouldn't differ much from a third one even when this third one could be completely unrelated.

Is this correct? Then in this case, how would you be able to tell whether you have a match or not?

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