Publications related to Random sampling of bandlimited signals on graphs

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Golnooshsadat Elhami, Michalina Wanda Pacholska

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is of ...

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

Quasi-random numbers for copula models

Mathieu Jacques David Cambou

The present work addresses the question how sampling algorithms for commonly applied copula models can be adapted to account for quasi-random numbers. Besides sampling methods such as the conditional distribution method (based on a one-to-one transformatio ...

Springer2017

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

Compressive Spectral Clustering

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

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

Riemannian Optimization For High-Dimensional Tensor Completion

Michael Maximilian Steinlechner

Tensor completion aims to reconstruct a high-dimensional data set where the vast majority of entries is missing. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem rest ...

Siam Publications2016

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

Too much success for recent groundbreaking epigenetic experiments

Gregory Francis

An article reporting statistical evidence for epigenetic transfer of learned behavior has important implications, if true. With random sampling, real effects do not always result in rejection of the null hypothesis, but the reported experiments were unifor ...

Genetics Society of America2014

Measuring the relative effect of factors affecting species distribution model predictions

Anthony Christopher Davison, Emeric Rolland Georges Thibaud

Species distribution models are increasingly used to address conservation questions, so their predictive capacity requires careful evaluation. Previous studies have shown how individual factors used in model construction can affect prediction. Although som ...

Wiley-Blackwell2014

Optimizing a remotely sensed proxy for plankton biomass in Lake Kivu

Andrea Rinaldo, Enrico Bertuzzo, Lorenzo Mari, Allyn Christine Knox

Many regions of the world, including inland lakes, present with suboptimal conditions for the remotely sensed retrieval of optical signals, thus challenging the limits of available satellite data-processing tools, such as atmospheric correction models (ACM ...

Taylor & Francis2014

Random sampling of bandlimited signals on graphs

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Sampling at unknown locations: Uniqueness and reconstruction under constraints

Safe Adaptive Importance Sampling

Quasi-random numbers for copula models

Shapes from Pixels

Compressive Spectral Clustering

Riemannian Optimization For High-Dimensional Tensor Completion

Random sampling of bandlimited signals on graphs

Too much success for recent groundbreaking epigenetic experiments

Measuring the relative effect of factors affecting species distribution model predictions

Optimizing a remotely sensed proxy for plankton biomass in Lake Kivu

Shapes from Pixels

Random sampling of bandlimited signals on graphs

Compressive Spectral Clustering

Too much success for recent groundbreaking epigenetic experiments

Quasi-random numbers for copula models

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Safe Adaptive Importance Sampling

Riemannian Optimization For High-Dimensional Tensor Completion

Measuring the relative effect of factors affecting species distribution model predictions

Optimizing a remotely sensed proxy for plankton biomass in Lake Kivu