Publications related to Compressive Spectral Clustering

A Novel Method for Sampling Bandlimited Graph Signals

Pascal Frossard, Pinar Akyazi, Dion Eustathios Olivier Tzamarias

In this paper we propose a novel vertex based sampling method for k-bandlimited signals lying on arbitrary graphs, that has a reasonable computational complexity and results in low reconstruction error. Our goal is to find the smallest set of vertices that ...

IEEE COMPUTER SOC2018

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling k-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, i.e., independent of the graph structure, and its performa ...

Elsevier2018

Testing Graph Clusterability: Algorithms and Lower Bounds

Mikhail Kapralov, Aidasadat Mousavifar, Ashish Hari Chiplunkar

We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generali ...

IEEE COMPUTER SOC2018

Real-time implementation with FPGA-based DAQ system of a probabilistic disruption predictor from scratch

Olivier Sauter, Ambrogio Fasoli, Basil Duval, Stefano Coda, Jonathan Graves, Yves Martin, Duccio Testa, Patrick Blanchard, Alessandro Pau, Cristian Sommariva, Henri Weisen, Richard Pitts, Yann Camenen, Jan Horacek, Javier García Hernández, Marco Wischmeier, Nicola Vianello, Mikhail Maslov, Federico Nespoli, Yao Zhou, David Pfefferlé, Davide Galassi, Antonio José Pereira de Figueiredo, Jonathan Marc Philippe Faustin, Liang Yao, Dalziel Joseph Wilson, Hamish William Patten, Samuel Lanthaler, Bernhard Sieglin, Otto Asunta

Real-time (RT) disruption prediction (DP) is essential to trigger mitigation actions that avoid irreversible damage to the devices. This paper deals with disruption mitigation alarms and performs the RT implementation of a probabilistic predictor. The RT i ...

2018

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Matthias Grossglauser, Lucas Maystre

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal sol ...

2017

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Matthieu Martin Jean-André Simeoni

Estimating intensity fields of stochastic phenomenons is of crucial interest in many scientific applications. Typical experimental setups involve an acquisition system, that subsequently filters and samples the probed intensity field. This equivalently def ...

2016

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

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

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Compressive Spectral Clustering

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A Novel Method for Sampling Bandlimited Graph Signals

Random sampling of bandlimited signals on graphs

Testing Graph Clusterability: Algorithms and Lower Bounds

Real-time implementation with FPGA-based DAQ system of a probabilistic disruption predictor from scratch

Safe Adaptive Importance Sampling

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Shapes from Pixels

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Random sampling of bandlimited signals on graphs

Random sampling of bandlimited signals on graphs

Safe Adaptive Importance Sampling

Random sampling of bandlimited signals on graphs

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Testing Graph Clusterability: Algorithms and Lower Bounds

Shapes from Pixels

A Novel Method for Sampling Bandlimited Graph Signals

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Real-time implementation with FPGA-based DAQ system of a probabilistic disruption predictor from scratch