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-FFT Recap (or, what am I expected to know?) - Learning Finite State Environments Avrim Blum 11/25/03

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FFT Recap The basic result: Given vectors –A = (a 0, a 1, a 2,..., a n-1 ), and –B = (b 0, b 1,..., b n-1 ), the FFT allows us in O(n log n) time to compute the convolution –C = (c 0, c 1,..., c 2n-2 ) where c j = a 0 b j + a 1 b j a j b 0. I.e., this is polynomial multiplication, where A,B,C are vectors of coefficients.

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How does it work? Compute F -1 (F(A) ¢ F(B)), where “F” is FFT. F(A) is evaluation of A at 1, m – is principal m th root of unity. m = 2n-1. E.g., = e /m. Or use modular arithmetic. –Able to do this quickly with divide-and-conquer. F(A) ¢ F(B) gives C(x) at these points. We then saw that F -1 = (1/m)F’, where F’ is same as F but using -1.

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Applications (not on test) signal analysis, lots moresignal analysislots more Pattern matching with don’t cares: –Given text string X = x 1,x 2,...,x n. x i 2 {0..25} –Given pattern Y = y 1,y 2,...,y k. y i 2 {0..25} [ {*}. –Want to find instances of Y inside X. Idea [Adam Kalai based on Karp-Rabin]: –Pick random R: r 1,r 2,...,r k, r i 2 1..N. E.g, N=n 2 –Set r i = 0 if y k-i+1 = *. –Let T = r 1 y k r k y 1. (can do mod p > N) –Now do convolution of R and X. See if any entries match T. Each entry at most 1/N chance of false positive.

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OK, on to machine learning...

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