2 . 3 Sketching using Locality Sensitive Hashing

Detecting Frequent Patterns in Video Using Partly Locality Sensitive Hashing

Frequent patterns in video are useful clues to learn previously unknown events in an unsupervised way. This paper presents a novel method for detecting relatively long variable-length frequent patterns in video efficiently. The major contribution of the paper is that Partly Locality Sensitive Hashing (PLSH) is proposed as a sparse sampling method to detect frequent patterns faster than the conv...

Faster Sieving for Shortest Lattice Vectors Using Spherical Locality-Sensitive Hashing

Recently, it was shown that angular locality-sensitive hashing (LSH) can be used to significantly speed up lattice sieving, leading to a heuristic time complexity for solving the shortest vector problem (SVP) of 2 (and space complexity 2. We study the possibility of applying other LSH methods to sieving, and show that with the spherical LSH method of Andoni et al. we can heuristically solve SVP...

Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets

We study embedding a subset K of the unit sphere to the Hamming cube {−1,+1}m . We characterize the tradeoff between distortion and sample complexity m in terms of the Gaussian width ω(K) of the set. For subspaces and several structured-sparse sets we show that Gaussian maps provide the optimal tradeoff m ∼ δω(K), in particular for δ distortion one needs m ≈ δd where d is the subspace dimension...

Locality-Sensitive Hashing

Multi-Level Spherical Locality Sensitive Hashing For Approximate Near Neighbors

This paper introduces “Multi-Level Spherical LSH”: parameter-free, a multi-level, data-dependant Locality Sensitive Hashing data structure for solving the Approximate Near Neighbors Problem (ANN). This data structure is a modified version multi-probe adaptive querying algorithm, with the potential of achieving a O(np + t) query run time, for all inputs n where t <= n. Keywords—Locality Sensitiv...