Concept

Locality-sensitive hashing

Related publications (33)

HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces

Akhil Arora, Arnab Bhattacharya, Piyush Kumar

Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due to the curse of dimensionality. A flavor of approximation is, therefore, necessary to practically solve the problem of nearest neighbor search. In this pap ...

ASSOC COMPUTING MACHINERY2018

This paper is concerned with low multilinear rank approximations to antisymmetric tensors, that is, multivariate arrays for which the entries change sign when permuting pairs of indices. We show which ranks can be attained by an antisymmetric tensor and di ...

Siam Publications2017

Large Scale Graph Learning from Smooth Signals

Nathanaël Perraudin, Vasilis Kalofolias

Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typically they are constructed either by connecting nearest samples, or by learnin ...

2017

, , ,

Cloud computing is becoming the preferred solution for efficiently dealing with the increasing amount of genomic data. Yet, outsourcing storage and processing of sensitive data, such as genomic data, comes with important concerns related to privacy and sec ...

Cryptology ePrint Archive: Report 2017/2282017

Low-latency Blockchain Consensus

Bryan Alexander Ford, Eleftherios Kokoris Kogias, Cristina Basescu

Blockchains are increasingly attractive due to their decentralization, yet inherent limitations of high latency, in the order of minutes, and attacks on consensus cap their practicality. We introduce Blinkchain, a Byzantine consensus protocol that relies o ...

2017

Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure

Michael Maximilian Steinlechner

In this thesis, we present a Riemannian framework for the solution of high-dimensional optimization problems with an underlying low-rank tensor structure. Here, the high-dimensionality refers to the size of the search space, while the cost function is scal ...

EPFL2016

MATHICSE Technical Report : Structure-preserving low multilinear rank approximation of antisymmetric tensors

Daniel Kressner

MATHICSE2016

Randomized Low-Memory Singular Value Projection

Volkan Cevher, Anastasios Kyrillidis

Affine rank minimization algorithms typically rely on calculating the gradient of a data error followed by a singular value decomposition at every iteration. Because these two steps are expensive, heuristics are often used for approximations that reduce co ...

2013

Search for Time-Dependent CPT Violation in Hadronic and Semileptonic B Decays

Olivier Schneider, Yiming Li, Aurelio Bay, Chao Wang, Tagir Aushev, Jun Yong Kim, Remi Léandre Albert Louvot, Ji Hyun Kim, Xiao Wang, Jing Li

We report a new sensitive search for C P T violation, which includes improved measurements of the C P T-violating parameter z and the total decay-width difference normalized to the averaged width delta(d) delta(d) of the two B-d mass eigenstates . The resu ...

Amer Physical Soc2012

Grassmann Hashing For Approximate Nearest Neighbor Search In High Dimensional Space

Lei Zhang, Xinchao Wang

Locality-Sensitive Hashing (LSH) approximates nearest neighbors in high dimensions by projecting original data into low-dimensional subspaces. The basic idea is to hash data samples to ensure that the probability of collision is much higher for samples tha ...

Ieee2011

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.