Let X be a set of n points of norm at most 1 in the Euclidean space R^ k, and suppose≥ 0.
An≥-distance sketch for X is a data structure that, given any two points of X enables one to …

We present the first sublinear memory sketch that can be queried to find the nearest
neighbors in a dataset. Our online sketching algorithm compresses an N element dataset to …

We study the problem of representing all distances between n points in \mathbbR^d, with
arbitrarily small distortion, using as few bits as possible. We give asymptotically tight bounds …

In this research we implement a system to monitor student attendance at South Ural State
University. Our system is based on the automatic recognition of students' faces using neural …

Abstract We consider the $(1+\epsilon) $-approximate nearest neighbor search problem:
given a set $ X $ of $ n $ points in a $ d $-dimensional space, build a data structure that …

We introduce a new distance-preserving compact representation of multi-dimensional point-
sets. Given n points in a d-dimensional space where each coordinate is represented using B …

Abstract The Planar Graph Metric Compression Problem is to compactly encode the
distances among k nodes in a planar graph of size n. Two naïve solutions are to store the …

Nearest neighbor searching systems are an integral part of many online applications,
including but not limited to pattern recognition, plagiarism detection, and recommender …

Motivated by the problem of compressing point sets into as few bits as possible while
maintaining information about approximate distances between points, we construct random …

We consider the problem of embedding a subset of into a low-dimensional Hamming cube
in an almost isometric way. We construct a simple, data-oblivious, and computationally …