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Say I have $n$ vectors $\{ z_i \in \mathbb{R}^D\}_{i=1}^n$ (where $n$ is very large and hence I can't do any calculation which scales as $n$) and I want to create $n$ vectors $\{x_i \in \mathbb{R}^d \}_{i=1}^n$ with $d$ as small as possible, such that $\forall i,j <z_i,z_j>$ is either very close to $<x_i,x_j>$ OR w.h.p $ <z_i,z_j> = <x_i,x_j>$.

  • Is there a standard answer known for this - by maybe using hashing?

  • One possible way might be to either deterministically or randomly from some distribution pick some $D-d$ coordinates and drop those coordinates from each $x_i$ to get the corresponding $z_i$ such that for each pair on either side either the inner-product is well approximated or is equal with high-probability over the way the coordinates are sampled. Is such a way of sampling known?


For the second option one other way to think would be to pick an unit magnitude gaussian vector at random $g \in \mathbb{R}^d \subset \mathbb{R}^D$ and to define $x_i$ as the projection of $z_i$ along $g$. Then indeed one has that $\mathbb{E}_g[ <x_i,x_j>] = <z_i,z_j>$.

But can this above be shown to be true with high concentration or be derandomized?

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  • $\begingroup$ I'm not an expert in this matter, but I am aware of the Johnson-Lindenstrauss lemma which gives guarantees similar to what you demand. I would post as an answer, but I honestly am not familiar enough with the inner workings to warrant a complete post. $\endgroup$ – Nicholas Mancuso Apr 22 '16 at 23:00
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I think your "possible way" is a known variant of "locality sensitive hashing", which is what you're looking for, I suspect.

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  • $\begingroup$ The "standard" LSH references I see do not seem to fit my question. So I am asking what is this "varant" that jbapple refers to. Unless something more specific is said, I don't know what to search for! $\endgroup$ – gradstudent Apr 19 '16 at 6:09
  • $\begingroup$ @gradstudent, the method of dropping vector components is referenced in the first paper on LSH. $\endgroup$ – jbapple Apr 19 '16 at 14:11
  • $\begingroup$ You mean somewhere in this paper? cs.princeton.edu/courses/archive/spring13/cos598C/Gionis.pdf Are they able to preserve all pair-wise inner-products of any arbitrary set of vectors? $\endgroup$ – gradstudent Apr 19 '16 at 14:46

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