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1-Bit Compressed Sensing.pdf

221_main_paper.pdf

🔗 Source: auai.org

📊 Size: 563 KB

📄 Pages: 10 pages

⬇️ Downloads: 144

Summary

Estimate a sparse signal from a few binary measurements.
Methodology: Non-convex sparsity-constrained program with a novel analysis that moves away from Gaussian width.
Key Results:
+ A simple algorithm guarantees an accurate approximation to the normalized signal with high probability.
+ Ensemble of new results addressing norm estimation, support recovery, and model misspecification.
+ Computational efficiency: one-step hard thresholding with low time complexity and memory footprint.
+ Statistical guarantee: near-optimal error rate under standard conditions.
Contributions:
+ Proposal of a sparsity-constrained non-convex program for 1-bit compressed sensing.
+ Novel analysis and theoretical results substantiated by numerical experiments.
Applications: High-dimensional statistics, compressed sensing, sparse approximation, norm estimation, and support recovery.

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Description

One-bit compressed sensing estimates a sparse signal from binary measurements. A novel analysis guarantees a simple algorithm's accuracy under the 2-metric with high probability. One-step hard thresholding solves the non-convex program efficiently.

Technical Information

File Format: PDF
File Size: 563 KB
Pages: 10
Language: EN
Total Downloads: 144
Last Updated: 1 week ago

Document Overview

This PDF document about 1-Bit Compressed Sensing provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into 1-Bit Compressed Sensing.