locality-sensitive-hash

I already have the algorithm to produce locality-sensitive hashes, but how should I bucket them to take advantage of their characteristics(i.e. similar elements have near hashes(with the hamming distance))? In the matlab code I found they simply create a distance matrix between the hashes of the points to search and the hashes of the points in the database, to simplify the code,while referencing a so called Charikar method for an actually good implementation of the search method. I tried to search for that, but I'm not sure how to apply to my case any of the methods I found(like the multi

Currently I'm studying how to find a nearest neighbor using Locality-sensitive hashing. However while I'm reading papers and searching the web I found two algorithms for doing this: 1- Use L number of hash tables with L number of random LSH functions, thus increasing the chance that two documents that are similar to get the same signature. For example if two documents are 80% similar, then there's an 80% chance that they will get the same signature from one LSH function. However if we use multiple LSH functions, then there's a higher chance to get the same signature for the documents from one

I read some paper about LSH and I know that is used for solving the approximated k-NN problem. We can divide the algorithm in two parts: Given a vector in D dimensions (where D is big) of any value, translate it with a set of N (where N<<D ) hash functions to a binary vector in N dimensions. Using hamming distance, apply some search technique on the set of given binary codes obtained from phase 1 to find the k-NN. The keypoint is that computing the hamming distance for vectors in N dimensions is fast using XOR. Anyway, I have two questions: Point 1. is still necessary if we use a binary

Citing the E2LSH manual ( it's not important that's about this specific library, this quote should be true for NN problem in general ): E 2LSH can be also used to solve the nearest neighbor problem, where, given the query q, the data structure is required the report the point in P that is closest to q. This can be done by creating several R-near neighbor data structures, for R = R1, R2, . . . Rt , where Rt should be greater than the maximum distance from any query point to its nearest neighbor. The nearest neighbor can be then recovered by querying the data structures in the increasing order

Are there any relatively simple to understand (and simple to implement) locality-sensitive hash examples in C/C++/Java/C#? I'd like to learn more about the concept and so want to try an implementation on a few text files just to see how it works, so I don't need anything high-performance or anything... just an example of a hash function that returns similar hashes for similar inputs. I can learn more from it by example afterwards. :) For strings you can use approximate matching algorithm. Generate a random string For all the strings compute their distance from that random shared string using

I'm trying to understand the section 5. of this paper about LSH, in particular how to bucket the generated hashes. Quoting the linked paper: Given bit vectors consisting of d bits each, we choose N = O(n 1/(1+epsilon) ) random permutations of the bits. For each random permutation σ, we maintain a sorted order O σ of the bit vectors, in lexicographic order of the bits permuted by σ. Given a query bit vector q, we find the approximate nearest neighbor by doing the following: For each permu- tation σ, we perform a binary search on O σ to locate the two bit vectors closest to q (in the