Finding Correlations in Subquadratic Time, with Applications to Learning Parities and Juntas
Gregory J. Valiant
Given a set of n d-dimensional Boolean vectors with the promise that the vectors are chosen uniformly at random with the exception of two vectors that have Pearson-correlation ρ (Hamming distance d · 1-ρ/2), how quickly can one find the two correlated vectors? We present an algorithm which, for any constants ε, ρ >; 0 and d >;>; logn/ρ2, finds the correlated pair with high probability, and runs in time O(n 3ω/4 + ϵ)1.8), where w2-O(ρ)), given by Paturi et al. [15], Locality Sensitive Hashing (LSH) [11] and the Bucketing Codes approach [6]. Applications and extensions of this basic algorithm yield improved algorithms for several other problems: ApproximateClosest Pair: For any sufficiently small constant ϵ >; 0, given n vectors in Rd, our algorithm returns a pair of vectors whose Euclidean distance differs from that of the closest pair by a factor of at most 1+ϵ, and runs in time O(n2-Θ(√ϵ)). The best previous algorithms (including LSH) have runtime O(n2-O(ϵ)). Learning Sparse Parity with Noise: Given samples from an instance of the learning parity with noise problem where each example has length n, the true parity set has size at most kkpoly(1/1-2η)0.8kpoly(1/1-2η). This is the first algorithm with no depenJence on η in the exponent of n, aside from the trivial brute-force algorithm. Learning k-Juntas with Noise: Given uniformly random length n Boolean vectors, together with a label, which is some function of just kkpoly(1/1-2η)0.8kpoly(1/1-2η), 2 which improves on the previous best of >; nk(1-2/2k)poly( 1/1-2η ), from [8]. Learning k-Juntas without Noise:1 Our results for learning sparse parities with noise imply an algorithm for learning juntas without noise with runtime n ω+ϵ/4kpoly(n)0.6kpoly(n), which improves on the runtime n ω+1/ω poly(n) ≈ n0.7kpoly(n) of Mossel n et al. [13].
Annual Symposium on Foundations of Computer Science•Oct 1, 2012•68 citations