Publications related to Compressible distributions for high-dimensional statistics

Autocalibrated Signal Reconstruction From Linear Measurements Using Adaptive Gamp

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Aurélien Bourquard

In this paper, we reconstruct signals from underdetermined linear measurements where the componentwise gains of the measurement system are unknown a priori. The reconstruction is performed through an adaptation of the message-passing algorithm called adapt ...

Ieee2013

Autocalibrated Signal Reconstruction from Linear Measurements Using Adaptive GAMP

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Aurélien Bourquard

In this paper, we reconstruct signals from underdetermined linear measurements where the componentwise gains of the measurement system are unknown a priori. The reconstruction is performed through an adaptation of the message-passing algorithm called adapt ...

IEEE2013

Beyond Moore-Penrose: Sparse Pseudoinverse

Martin Vetterli, Mihailo Kolundzija, Ivan Dokmanic

Frequently, we use the Moore-Penrose pseudoinverse (MPP) even in cases when we do not require all of its defining properties. But if the running time and the storage size are critical, we can do better. By discarding some constraints needed for the MPP, we ...

2013

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We introduce a new wavelet-based method for the implementation of Total-Variation-type denoising. The data term is least-squares, while the regularization term is gradient-based. The particularity of our method is to exploit a link between the discrete gra ...

IEEE2012

Mmse Denoising Of Sparse Levy Processes Via Message Passing

Michaël Unser, Arash Amini, Ulugbek Kamilov

Many recent algorithms for sparse signal recovery can be interpreted as maximum-a-posteriori (MAP) estimators relying on some specific priors. From this Bayesian perspective, state-of-the-art methods based on discrete-gradient regularizers, such as total-v ...

Ieee2012

MMSE Denoising of Sparse Lévy Processes via Message Passing

Michaël Unser, Azin Amini, Ulugbek Kamilov

Many recent algorithms for sparse signal recovery can be interpreted as maximum-a-posteriori (MAP) estimators relying on some specific priors. From this Bayesian perspective, state-of-the-art methods based on discrete-gradient regularizers, such as total- ...

IEEE2012

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

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

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider recovery of a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model which can efficiently restricts the degrees of freedom of data and, at the same time, is generic so that f ...

2012

Sparse diffusion LMS for distributed adaptive estimation

Ali H. Sayed

The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, ...

IEEE2012

Evidence for Alternative Hypotheses

Stephan Morgenthaler, Robert Staudte

Most researchers want evidence for the direction of an effect, not evidence against a point null hypothesis. Such evidence is ideally on a scale that is easily interpretable, with an accompanying standard error. Further, the evidence from identical experim ...

Springer2012

Compressible distributions for high-dimensional statistics

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Autocalibrated Signal Reconstruction From Linear Measurements Using Adaptive Gamp

Autocalibrated Signal Reconstruction from Linear Measurements Using Adaptive GAMP

Beyond Moore-Penrose: Sparse Pseudoinverse

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Mmse Denoising Of Sparse Levy Processes Via Message Passing

MMSE Denoising of Sparse Lévy Processes via Message Passing

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

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

Sparse diffusion LMS for distributed adaptive estimation

Evidence for Alternative Hypotheses

Beyond Moore-Penrose: Sparse Pseudoinverse

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Autocalibrated Signal Reconstruction from Linear Measurements Using Adaptive GAMP

Mmse Denoising Of Sparse Levy Processes Via Message Passing

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

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

Sparse diffusion LMS for distributed adaptive estimation

Evidence for Alternative Hypotheses

Autocalibrated Signal Reconstruction From Linear Measurements Using Adaptive Gamp

MMSE Denoising of Sparse Lévy Processes via Message Passing