128 citations · 738 across the 58 of their papers we have counts for
5 papers · 1 filter
Sketched SVD: Recovering Spectral Features from Compressive Measurements
Anna C. Gilbert, Jae Young Park, Michael B. Wakin
We consider a streaming data model in which n sensors observe individual streams of data, presented in a turnstile model. Our goal is to analyze the singular value decomposition (S…
Technical Report: Observability with Random Observations
Borhan M. Sanandaji, Michael B. Wakin, Tyrone L. Vincent
Recovery of the initial state of a high-dimensional system can require a large number of measurements. In this paper, we explain how this burden can be significantly reduced when r…
The Restricted Isometry Property for Random Block Diagonal Matrices
Armin Eftekhari, Han Lun Yap, Christopher J. Rozell +1
In Compressive Sensing, the Restricted Isometry Property (RIP) ensures that robust recovery of sparse vectors is possible from noisy, undersampled measurements via computationally…
Stable Manifold Embeddings with Structured Random Matrices
Han Lun Yap, Michael B. Wakin, Christopher J. Rozell
The fields of compressed sensing (CS) and matrix completion have shown that high-dimensional signals with sparse or low-rank structure can be effectively projected into a low-dimen…
Signal Space CoSaMP for Sparse Recovery with Redundant Dictionaries
Mark A. Davenport, Deanna Needell, Michael B. Wakin
Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. The bulk of the CS literature has focused on the case where the acquired signal…