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An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing
Christopher A. Metzler, Philip Schniter, Richard G. Baraniuk
Generalized Vector Approximate Message Passing (GVAMP) is an efficient iterative algorithm for approximately minimum-mean-squared-error estimation of a random vector $\mathbf{x}\si…
One-Bit Compressive Sensing of Dictionary-Sparse Signals
Rich Baraniuk, Simon Foucart, Deanna Needell +2
One-bit compressive sensing has extended the scope of sparse recovery by showing that sparse signals can be accurately reconstructed even when their linear measurements are subject…
An Information-Theoretic Measure of Dependency Among Variables in Large Datasets
Ali Mousavi, Richard G. Baraniuk
The maximal information coefficient (MIC), which measures the amount of dependence between two variables, is able to detect both linear and non-linear associations. However, comput…
Self-Expressive Decompositions for Matrix Approximation and Clustering
Eva L. Dyer, Tom A. Goldstein, Raajen Patel +2
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure…
Exponential decay of reconstruction error from binary measurements of sparse signals
Richard Baraniuk, Simon Foucart, Deanna Needell +2
Binary measurements arise naturally in a variety of statistical and engineering applications. They may be inherent to the problem---e.g., in determining the relationship between ge…
From Denoising to Compressed Sensing
Christopher A. Metzler, Arian Maleki, Richard G. Baraniuk
A denoising algorithm seeks to remove noise, errors, or perturbations from a signal. Extensive research has been devoted to this arena over the last several decades, and as a resul…