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Distributed Similarity Search in High Dimensions Using Locality Sensitive Hashing

Publications associées (44)

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Graph-based Methods for Visualization and Clustering

Johann Paratte

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017

Expanding Requirements Through Observation: An Experience Report

Gil Regev, Alain Wegmann, Nina Desnica

In this paper, we describe our use of ethnographic techniques for identifying requirements for a web-based system for automatic updates and data exchange in a financial investment company. During a six-month long internship, we observed stakeholders in the ...

CEUR Workshop Proceedings2017

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

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Personal Learning with Social Media

Na Li

Social media platforms are increasingly used in recent years to support learning activities, especially for the construction of activity- and learner-centric personal learning environments (PLEs). This thesis investigates the solutions to four essential de ...

EPFL2015

RFID seeking: Finding a lost tag rather than only detecting its missing

Chen Zhang, Qiang Wang, Jian Xu, Lei Xie

This paper proposes a novel type of RFID application, i.e., RFID seeking. Several existing types of RFID applications such as monitoring, searching, locating/navigating, are similar with RFID seeking. However, they are either inapplicable or vulnerable for ...

Elsevier2014

The Integration of an RE Method and AHP: A Pilot Study in a Large Swiss Bank

Gil Regev, Alain Wegmann, Arash Golnam, Sofia Kyriakopoulou

This paper reports on a pilot study of the integration between the Systemic Enterprise Architecture Method (SEAM) and the Analytic Hierarchy Process (AHP) in a requirements engineering project. The objective of the project, conducted in one of the major ba ...

IEEE2013

Low-rank Matrix Approximation Using Point-wise Operators

Arash Amini, Amin Karbasi

Extracting low dimensional structure from high dimensional data arises in many applications such as machine learning, statistical pattern recognition, wireless sensor networks, and data compression. If the data is restricted to a lower dimensional subspace ...

Institute of Electrical and Electronics Engineers2012

Music Recommendation to Groups

Lucas Maystre

First we present Unison, a conceptual music recommender system for groups of people; the system aims at generating a playlist that takes musical tastes of all the group members into account. We discuss both theoretical and practical concerns related to suc ...

2012

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