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Concept

Partitionnement spectral

Publications associées (29)

Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar

Rachid Guerraoui, Anne-Marie Kermarrec, Tao Lin, Rhicheek Patra

Recommenders, as widely implemented nowadays by major e-commerce players like Netflix or Amazon, use collaborative filtering to suggest the most relevant items to their users. Clearly, the effectiveness of recommenders depends on the data they can exploit, ...

Assoc Computing Machinery2017

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first

k

eigenvectors of the similarity matrix' Laplacian, whose computati ...

Ieee2016

Low-rank methods for parameter-dependent eigenvalue problems and matrix equations

Petar Sirkovic

The focus of this thesis is on developing efficient algorithms for two important problems arising in model reduction, estimation of the smallest eigenvalue for a parameter-dependent Hermitian matrix and solving large-scale linear matrix equations, by extra ...

EPFL2016

A Bi-Criteria Approximation Algorithm for k-Means

Justin Dean Ward

We consider the classical k-means clustering problem in the setting of bi-criteria approximation, in which an algorithm is allowed to output beta*k > k clusters, and must produce a clustering with cost at most alpha times the to the cost of the optimal set ...

Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik2016

Compressive Spectral Clustering

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a fea ...

2016

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

Frank Grégoire Jean de Morsier

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth ...

EPFL2014

Non-linear Low-rank and Sparse Representation for Hyperspectral Image Analysis

Jean-Philippe Thiran, Devis Tuia, Volker Gass, Frank Grégoire Jean de Morsier, Maurice Borgeaud

In this paper, we tackle the problem of unsupervised classification of hyperspectral images. We propose a clustering method based on graphs representing the data structure, which is assumed to be an union of multiple manifolds. The method constraints the p ...

2014

Detection of Floods In Sar Images with Non-Linear Kernel Clustering and Topographic Prior

Jean-Philippe Thiran, Devis Tuia, Frank Grégoire Jean de Morsier, Maurice Borgeaud

After a major flood catastrophe, a precious information is the delineation of the affected areas. Remote sensing imagery, especially synthetic aperture radar, allows to obtain a global and complete view of the situation. However, the detection of the flood ...

2013

Discovering places of interest in everyday life from smartphone data

Daniel Gatica-Perez

In this paper, a new framework to discover places-of-interest from multimodal mobile phone data is presented. Mobile phones have been used as sensors to obtain location information from users’ real lives. A place-of-interest is defined as a location where ...

Springer2013

Publications associées (29)

Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar

Rachid Guerraoui, Anne-Marie Kermarrec, Tao Lin, Rhicheek Patra

Assoc Computing Machinery2017

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first

k

eigenvectors of the similarity matrix' Laplacian, whose computati ...

Ieee2016