cs345-lsh-2

cs345-lsh-2 - Locality-Sensitive Hashing Basic Technique...

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Unformatted text preview: Locality-Sensitive Hashing Basic Technique Hamming-LSH Applications 1 Finding Similar Pairs Suppose we have in main memory data representing a large number of objects. May be the objects themselves (e.g., summaries of faces). May be signatures as in minhashing. We want to compare each to each, finding those pairs that are sufficiently similar. 2 Candidate Generation From Minhash Signatures Pick a similarity threshold s, a fraction < 1. A pair of columns c and d is a candidate pair if their signatures agree in at least fraction s of the rows. I.e., M (i, c ) = M (i, d ) for at least fraction s values of i. 3 Candidate Generation --- (2) For images, a pair of vectors is a candidate if they differ by at most a small threshold t in at least s % of the components. For entity records, a pair is a candidate if the sum of similarity scores of corresponding components exceeds a threshold. 4 The Problem with Checking for Candidates While the signatures of all columns may fit in main memory, comparing the signatures of all pairs of columns is quadratic in the number of columns. Example: 106 columns implies 5*1011 comparisons. At 1 microsecond/comparison: 6 days. 5 Solutions 1. Divide-Compute-Merge (DCM) uses external sorting, merging. 2. Locality-Sensitive Hashing (LSH) can be carried out in main memory, but admits some false negatives. 3. Hamming LSH --- a variant LSH method. 6 Divide-Compute-Merge Designed for “shingles” and docs. At each stage, divide data into batches that fit in main memory. Operate on individual batches and write out partial results to disk. Merge partial results from disk. 7 DCM Steps doc1: s11,s12,…,s1k doc2: s21,s22,…,s2k … Invert s11,doc1 s12,doc1 … s1k,doc1 s21,doc2 … sort on shingleId t1,doc11 t1,doc12 … t2,doc21 t2,doc22 … Invert and pair doc11,doc12,1 doc11,doc12,2 doc11,doc12,1 doc11,doc13,10 … … Merge doc11,doc13,1 … doc11,doc12,1 doc11,doc13,1 … doc21,doc22,1 … sort on <docId1, docId2> 8 DCM Summary 1. Start with the pairs <shingleId, docId>. 2. Sort by shingleId. 3. In a sequential scan, generate triplets <docId1, docId2, 1> for pairs of docs that share a shingle. 4. Sort on <docId1, docId2>. 5. Merge triplets with common docIds to generate triplets of the form <docId1,docId2,count>. 6. Output document pairs with count > threshold. 9 Some Optimizations “Invert and Pair” is the most expensive step. Speed it up by eliminating very common shingles. “the”, “404 not found”, “<A HREF”, etc. Also, eliminate exact-duplicate docs first. 10 Locality-Sensitive Hashing Big idea: hash columns of signature matrix M several times. Arrange that (only) similar columns are likely to hash to the same bucket. Candidate pairs are those that hash at least once to the same bucket. 11 Partition Into Bands r rows per band b bands Matrix M 12 Partition into Bands --- (2) Divide matrix M into b bands of r rows. For each band, hash its portion of each column to a hash table with k buckets. Candidate column pairs are those that hash to the same bucket for ≥ 1 band. Tune b and r to catch most similar pairs, but few nonsimilar pairs. 13 Buckets Matrix M r rows b bands 14 Simplifying Assumption There are enough buckets that columns are unlikely to hash to the same bucket unless they are identical in a particular band. Hereafter, we assume that “same bucket” means “identical.” 15 Example Suppose 100,000 columns. Signatures of 100 integers. Therefore, signatures take 40Mb. But 5,000,000,000 pairs of signatures can take a while to compare. Choose 20 bands of 5 integers/band. 16 Suppose C1, C2 are 80% Similar Probability C1, C2 identical in one particular band: (0.8)5 = 0.328. Probability C1, C2 are not similar in any of the 20 bands: (1-0.328)20 = .00035 . i.e., we miss about 1/3000th of the 80%similar column pairs. 17 Suppose C1, C2 Only 40% Similar Probability C1, C2 identical in any one particular band: (0.4)5 = 0.01 . Probability C1, C2 identical in ≥ 1 of 20 bands: ≤ 20 * 0.01 = 0.2 . But false positives much lower for similarities << 40%. 18 LSH Involves a Tradeoff Pick the number of minhashes, the number of bands, and the number of rows per band to balance false positives/negatives. Example: if we had fewer than 20 bands, the number of false positives would go down, but the number of false negatives would go up. 19 LSH --- Graphically Example Target: All pairs with Sim > t. Suppose we use only one hash function: 1.0 1.0 Ideal Sim Prob. 0.0 Prob. Sim s t 1.0 0.0 t 1.0 Partition into bands gives us: 1.0 1 – (1 – sr)b Sim s t 1.0 Prob. 0.0 t ~ (1/b)1/r 20 LSH Summary Tune to get almost all pairs with similar signatures, but eliminate most pairs that do not have similar signatures. Check in main memory that candidate pairs really do have similar signatures. Optional: In another pass through data, check that the remaining candidate pairs really are similar columns . 21 New Topic: Hamming LSH An alternative to minhash + LSH. Takes advantage of the fact that if columns are not sparse, random rows serve as a good signature. Trick: create data matrices of exponentially decreasing sizes, increasing densities. 22 Amplification of 1’s Hamming LSH constructs a series of matrices, each with half as many rows, by OR-ing together pairs of rows. Candidate pairs from each matrix have (say) between 20% - 80% 1’s and are similar in selected 100 rows. 20%-80% OK for similarity thresholds ≥ 0.5. • Otherwise, two “similar” columns with widely differing numbers of 1’s could fail to both be in range for at least one matrix. 23 Example 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 24 Using Hamming LSH Construct the sequence of matrices. If there are R rows, then log2R matrices. Total work = twice that of reading the original matrix. Use standard LSH on a random selection of rows to identify similar columns in each matrix, but restricted to columns of “medium” density. 25 LSH for Other Applications 1. Face recognition from 1000 measurements/face. 2. Entity resolution from name-addressphone records. General principle: find many hash functions for elements; candidate pairs share a bucket for > 1 hash. 26 Face-Recognition Hash Functions 1. Pick a set of r of the 1000 measurements. 2. Each bucket corresponds to a range of values for each of the r measurements. 3. Hash a vector to the bucket such that each of its r components is in-range. 4. Optional: if near the edge of a range, also hash to an adjacent bucket. 27 One bucket, for (x,y) if 10<x<16 and 0<y<4 Example: r = 2 10-16 17-23 24-30 31-37 38-44 (27,9) goes here. 0-4 5-9 Maybe put a copy here, too. 10-14 15-19 28 Many-One Face Lookup As for boolean matrices, use many different hash functions. Each based on a different set of the 1000 measurements. Each bucket of each hash function points to the images that hash to that bucket. 29 Face Lookup --- (2) Given a new image (the probe ), hash it according to all the hash functions. Any member of any one of its buckets is a candidate. For each candidate, count the number of components in which the candidate and probe are close. Match if #components > threshold. 30 Hashing the Probe probe Look in all these buckets h1 h2 h3 h4 h5 31 Many-Many Problem Make each pair of images that are in the same bucket according to any hash function be a candidate pair. Score each candidate pair as for the many-one problem. 32 Entity Resolution You don’t have the convenient multidimensional view of data that you do for “face-recognition” or “similarcolumns.” We actually used an LSH-inspired simplification. 33 Entity Resolution --- (2) Three hash functions: 1. One bucket for each name string. 2. One bucket for each address string. 3. One bucket for each phone string. A pair is a candidate iff they mapped to the same bucket for at least one of the three hashes. 34 ...
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