Talk:Locality-sensitive hashing

message: The Nilsimsa Hash does not really fit into the LSH definition(s) (Indyk's or Charikar's). Consequently, there is no way to plug into the common framework of LSH and obtain good index-size and query performance guarantees --- one of the strengths of LSH approaches. Hence, I suggest this section should be removed, or at least not in the current place (in the same level with random projection, simhash, and p-stable based lsh).  
: Is there a name for the more general category that includes Nilsima hash, LSH, LPH, TLSH, etc.? --DavidCary (talk) 16:52, 22 August 2016 (UTC)
thread_title:  Suggestion to remove Nilsimsa Hash section 

message: Just made the page. There are some variations among definitions of LSH - I am using Charikar's. Flamholz 19:40, 6 June 2007 (UTC)
replies: text: Do you have a reference to a "better" definition? Please add that reference to the article. Thank you. --DavidCary (talk) 16:52, 22 August 2016 (UTC)
thread_title: Untitled

message: I don't think the current definition really makes sense, although maybe it could be modified a little to work.
Specifically: for a metric phi(x,y), we have phi(x,y)->0 (intuitively) as x->y.  But if Pr[h(x)=h(y)] -> 0 as x->y, that's bad!  I mean, that is just about the opposite of a locality-sensitive hash.
One fix might be to say Pr[h(a) = h(b)] = 1 - phi(a,b) instead.
Although it would also be nice to allow for general boolean combinations of hashes, such as simultaneously hashing to many different values, and calling it a hit if some combination thereof actually collide.  —Preceding unsigned comment added by PhiloMath (talk • contribs) 07:14, 5 December 2007 (UTC)
thread_title:  Definition of an LSH 

message: We added the Indyk-Motwani definition of the LSH family, plus an LSH family for the Hamming space (by bit sampling), as well as the LSH algorithm for the nearest neighbor search (approximate). Alex and Piotr.
128.30.48.53 (talk) 02:13, 7 February 2008 (UTC)
thread_title:  Added another variant of LSH definition 

message: in the last section:
LSH Algorithm for the Nearest Neighbor Search
... it is being claimed that : ....
query time: O(L(kt+dnP_2^k));
i am trying to figure out from where does the P_2 comes from... why is the probability for colision is P_2 ?
can someone please shed some light on this?  —Preceding unsigned comment added by Caligola0 (talk • contribs) 18:01, 18 June 2009 (UTC)
replies: text: I agree -- what is the meaning of P_1, P_2, and d? --DavidCary (talk) 16:52, 22 August 2016 (UTC)
replies: text:  d is the dimension of the data-points, P_1 is the probability that two close points (distance \le R) collide. P_2 the probability that two far points (distance \ge cR) collide. --Thomasda (talk) 18:13, 3 November 2021 (UTC)
thread_title:  Hey guys, i have a question. 

message: hi - could someone clarify the relation to Vector Quantization please? --mcld (talk) 09:21, 8 April 2010 (UTC)
thread_title:  Relation to Vector Quantization? 

message: I suggest merging locality-preserving hashing into locality-sensitive hashing.
There seems to be enough WP:OVERLAP that a single article can cover both, and clarify the distinction (if any) between them.
--DavidCary (talk) 15:50, 22 August 2016 (UTC)
replies: text: Klbrain (talk) 08:13, 10 May 2018 (UTC)
thread_title:  merge 

message: How is 1-x/\pi "closely related" to cos(x) for small x? It is surely not a Taylor expansion, or anything of that sort. How is this even a relevant comment at this point? — Preceding unsigned comment added by 37.24.141.200  (talk) 21:43, 2 September 2016‎ (UTC) 
:That's a referenced example of method; I agree that the approximation is not the Taylor expansion, but it is the method used by the paper. The comment about the relationship between 1-x/\pi and cos(x) is necessary to support the final statement in the section:"Two vectors' bits match with probability proportional to the cosine of the angle between them". Klbrain (talk) 09:09, 4 April 2018 (UTC)
thread_title:  Random projection 

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thread_title:  External links modified 

message: The lead now refers to high-dimension data. I've spent 40 years as a software engineer, and know what ordinary hash coding is all about. And I thought I knew what a dimension is (an orthogonal axis in a graph, or a measure of the wiggliness of a line in fractal theory), but I haven't seen it used to refer to data before, possibly because I've never done any business programming (only systems and tools programming). Perhaps a brief definition could be added here? Just something that could distinguish high from low dimension data. For example, is the data set {1, 2, 3} high or low dimension? Is the data list (0:3, 1: 8, 2: -3) high or low dimension?. I have no idea, but I think if the lead is going to use this term, it should give at least one example, if nothing else. David Spector (talk) 23:29, 12 November 2021 (UTC)
thread_title:  High-dimension data? 

message: No explanation about function f at
:d(p,q) < d(q,r) \Rightarrow |f(p) - f(q)| < |f(q) - f(r)|
Please add explanation Krauss (talk) 11:49, 27 August 2022 (UTC)
replies: text: I agree that such an abstract function can use an explanation. David Spector (talk) 17:09, 27 August 2022 (UTC)
thread_title:  f=?