Publications related to Learning with tensors: a framework based on convex optimization and spectral regularization

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Multilinear Low-Rank Tensors on Graphs & Applications

Pierre Vandergheynst, Nauman Shahid, Francesco Grassi

We propose a new framework for the analysis of low- rank tensors which lies at the intersection of spectral graph theory and signal processing. As a first step, we present a new graph based low-rank decomposition which approximates the classical low-rank S ...

2016

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In recent works, sparse models and convex optimization techniques have been applied to radio-interferometric (RI) imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. In this talk, I will review our latest contribut ...

2015

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability

Alireza Karimi

In this paper, a new algorithm is proposed for the design of a family of controllers to be used within an adaptive switching control scheme. The resulting switching controller is able to attenuate the effects of disturbances having uncertain and possibly t ...

2015

Convex Optimization for Big Data

Volkan Cevher

This article reviews recent advances in convex optimization algorithms for Big Data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniqu ...

Institute of Electrical and Electronics Engineers2014

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

PURIFY: A new algorithmic framework for next-generation radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

In recent works, compressed sensing (CS) and convex opti- mization techniques have been applied to radio-interferometric imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. We review our latest contributions [1, 2, ...

Ieee2014

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

Rémi Gribonval, Gilles Puy

We investigate a compressive sensing system in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on a few unknown (but sparse) signals. We extend our earlier s ...

2013

A convex optimization approach for image recovery from nonlinear measurements in optical interferometry

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Anna Auria Rasclosa

Image recovery in optical interferometry is an ill-posed nonlinear inverse problem arising from incomplete power spectrum and bi-spectrum measurements. We formulate a linear version of the problem for the order-3 tensor formed by the tensor product of the ...

2013

Learning Categories from Few Examples with Multi Model Knowledge Transfer

Barbara Caputo, Tatiana Tommasi

Learning a visual object category from few samples is a compelling and challenging problem. In several real-world applications collecting many annotated data is costly and not always possible. However a small training set does not allow to cover the high i ...

Idiap2013

Learning with tensors: a framework based on convex optimization and spectral regularization

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Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Multilinear Low-Rank Tensors on Graphs & Applications

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability

Convex Optimization for Big Data

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

PURIFY: A new algorithmic framework for next-generation radio-interferometric imaging

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

A convex optimization approach for image recovery from nonlinear measurements in optical interferometry

Learning Categories from Few Examples with Multi Model Knowledge Transfer

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

Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Learning Categories from Few Examples with Multi Model Knowledge Transfer

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

PURIFY: A new algorithmic framework for next-generation radio-interferometric imaging

Multilinear Low-Rank Tensors on Graphs & Applications

Design of a Switching Controller for Adaptive Disturbance Attenuation with Guaranteed Stability

Convex Optimization for Big Data

A convex optimization approach for image recovery from nonlinear measurements in optical interferometry