The Noise Collector for sparse recovery in high dimensions
Abstract
The ability to detect sparse signals from noisy, high-dimensional data is a top priority in modern science and engineering. For optimal results, current approaches need to tune parameters that depend on the level of noise, which is often difficult to estimate. We develop a parameter-free, computationally efficient, ℓ 1 -norm minimization approach that has a zero false discovery rate (no false positives) with high probability for any level of noise while it detects the exact location of sparse signals when the noise is not too large.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 11, 2020
- Source ID
- 10.1073/pnas.1913995117
Entities
People
- Alexei Novikov
- Chrysoula Tsogka
- George Papanicolaou
- Miguel Moscoso
Organizations
- Air Force Office of Scientific Research
- National Science Foundation Directorate for Mathematical & Physical Sciences
- Pennsylvania State University
- Stanford University
- Universidad Carlos III de Madrid
- University of California