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Consistent Parameter Estimation for LASSO and Approximate Message Passing
Ali Mousavi, Arian Maleki, Richard G. Baraniuk
We consider the problem of recovering a vector from random and noisy linear observations , where is the measurement matrix and is no…
FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation
M. Salman Asif, Ali Ayremlou, Aswin Sankaranarayanan +2
FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera where an ima…
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…
A Deep Learning Approach to Structured Signal Recovery
Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk
In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse rep…
oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
Raajen Patel, Thomas A. Goldstein, Eva L. Dyer +2
Kernel matrices (e.g. Gram or similarity matrices) are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction. For large dataset…
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…