10similarity3 - 1 Theory of LSH Distance Measures LS...

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Unformatted text preview: 1 Theory of LSH Distance Measures LS Families of Hash Functions S-Curves 2 Distance Measures Generalized LSH is based on some kind of distance between points. Similar points are close. Two major classes of distance measure: 1. Euclidean 2. Non-Euclidean 3 Euclidean Vs. Non-Euclidean A Euclidean space has some number of real-valued dimensions and dense points. There is a notion of average of two points. A Euclidean distance is based on the locations of points in such a space. A Non-Euclidean distance is based on properties of points, but not their location in a space. 4 Axioms of a Distance Measure d is a distance measure if it is a function from pairs of points to real numbers such that: 1. d(x,y) > 0. 2. d(x,y) = 0 iff x = y. 3. d(x,y) = d(y,x). 4. d(x,y) < d(x,z) + d(z,y) ( triangle inequality ). 5 Some Euclidean Distances L norm : d(x,y) = square root of the 6 Examples of Euclidean Distances a = (5,5) b = (9,8) 4 3 5 7 Another Euclidean Distance L norm : d(x,y) = the maximum of the 8 Non-Euclidean Distances Jaccard distance for sets = 1 minus Jaccard similarity. Cosine distance = angle between vectors from the origin to the points in question. Edit distance = number of inserts and deletes to change one string into another. Hamming Distance = number of positions in which bit vectors differ. 9 Jaccard Distance for Sets (Bit-Vectors) Example : p 1 = 10111; p 2 = 10011. Size of intersection = 3; size of union = 4, Jaccard similarity (not distance) = 3/4. d(x,y) = 1 (Jaccard similarity) = 1/4. 10 Why J.D. Is a Distance Measure d(x,x) = 0 because x x = x x. d(x,y) = d(y,x) because union and intersection are symmetric. d(x,y) > 0 because |x y| < |x y|. d(x,y) < d(x,z) + d(z,y) trickier next slide. 11 Triangle Inequality for J.D. 1 - |x z| + 1 - |y z| > 1 -|x y| |x z| |y z| |x y| Remember : |a b|/|a b| = probability that minhash(a) = minhash(b). Thus, 1 - |a b|/|a b| = probability that minhash(a) minhash(b). 12 Triangle Inequality (2) Claim : prob[minhash(x) minhash(y)] < prob[minhash(x) minhash(z)] + prob[minhash(z) minhash(y)] Proof : whenever minhash(x) minhash(y), at least one of minhash(x) minhash(z) and minhash(z) minhash(y) must be true. 13 Cosine Distance Think of a point as a vector from the origin (0,0,,0) to its location....
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10similarity3 - 1 Theory of LSH Distance Measures LS...

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