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LSH At Large - Distributed KNN Search in High Dimensions

Publications associées (36)

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Graph Embedded Nonparametric Mutual Information For Supervised Dimensionality Reduction

Nikolaos Arvanitopoulos Darginis

In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their cor- responding class labels. The MI is a powerful criterion that can be used as a proxy to ...

Institute of Electrical and Electronics Engineers2015

Using sketch-map coordinates to analyze and bias molecular dynamics simulations

Michele Ceriotti, Gareth Aneurin Tribello, Michele Parrinello

When examining complex problems, such as the folding of proteins, coarse grained descriptions of the system drive our investigation and help us to rationalize the results. Oftentimes collective variables (CVs), derived through some chemical intuition about ...

National Academy of Sciences2012

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

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

Efficient Processing of Ranking Queries in Novel Applications

Parisa Haghani

Ranking queries, which return only a subset of results matching a user query, have been studied extensively in the past decade due to their importance in a wide range of applications. In this thesis, we study ranking queries in novel environments and setti ...

EPFL2010

Distributed Similarity Search in High Dimensions Using Locality Sensitive Hashing

Karl Aberer, Sebastian Michel, Parisa Haghani

In this paper we consider distributed K-Nearest Neighbor (KNN) search and range query processing in high dimensional data. Our approach is based on Locality Sensitive Hashing (LSH) which has proven very efficient in answering KNN queries in centralized set ...

2009

Storing and Indexing Spatial Data in P2P Systems

The peer-to-peer (P2P) paradigm has become very popular for storing and sharing information in a totally decentralized manner. At first, research focused on P2P systems that host 1D data. Nowadays, the need for P2P applications with multidimensional data h ...

Institute of Electrical and Electronics Engineers2009

Designing peer-to-peer overlays

Sarunas Girdzijauskas

The Small-World phenomenon, well known under the phrase "six degrees of separation", has been for a long time under the spotlight of investigation. The fact that our social network is closely-knitted and that any two people are linked by a short chain of a ...

EPFL2009

Slicing Distributed Systems

Anne-Marie Kermarrec, Vincent Gramoli

Peer-to-peer (P2P) architectures are popular for tasks such as collaborative download, VoIP telephony, and backup. To maximize performance in the face of widely variable storage capacities and bandwidths, such systems typically need to shift work from poor ...

2009

Sharing of probabilistically correlated data in peer-to-peer networks

Roman Schmidt

The impact of Peer-to-Peer (P2P) networks on the Internet landscape is undisputed. It has led to a series of new applications, e.g., as part of the so-called Web 2.0. The shift from the classical client-server based paradigm of the Internet, with a clear d ...

EPFL2008