Publications related to Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Acoustic DoA Estimation by One Unsophisticated Sensor

Martin Vetterli, Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend t ...

Springer Verlag2017

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Compressive PCA for Low-Rank Matrices on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid

We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong to the span of the first few eigenvectors of the graphs constructed between ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Structure Modeling of High Dimensional Data

Mahdad Hosseini Kamal

The digitization of our most common appliances has led to a literal data deluge, some- times referred to as Big Data. The ever increasing volume of data we generate, coupled with our desire to exploit it ever faster, forces us to come up with innovative da ...

EPFL2015

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Volkan Cevher

This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease stati ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Michel Bierlaire, Anna Fernández Antolín

Endogeneity is an important issue that often arises in discrete choice models leading to biased estimates of the parameters. We propose the extended multiple indicator solution (EMIS) methodology to correct for it and exemplify it with a case study using r ...

2015

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Pierre Vandergheynst, Mahdad Hosseini Kamal

We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensio ...

Springer-Verlag Berlin2015

Total Variation Regularization of Shape Signals

Martin Kurt Storath

This paper introduces the concept of shape signals, i.e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals. The proposed formulation can be seen as the shape-va ...

IEEE2015

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

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Acoustic DoA Estimation by One Unsophisticated Sensor

Fast Robust PCA on Graphs

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Estimation Error of the Constrained Lasso

Compressive PCA for Low-Rank Matrices on Graphs

Structure Modeling of High Dimensional Data

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Total Variation Regularization of Shape Signals

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Fast Robust PCA on Graphs

Total Variation Regularization of Shape Signals

Acoustic DoA Estimation by One Unsophisticated Sensor

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

Compressive PCA for Low-Rank Matrices on Graphs

Structure Modeling of High Dimensional Data

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Estimation Error of the Constrained Lasso