# New Algorithms and Improved Guarantees for One-Bit Compressed Sensing on Manifolds

## TL;DR

This paper introduces new algorithms for one-bit compressed sensing on manifolds, providing improved guarantees and extending measurement types, with theoretical analysis and numerical validation.

## Contribution

It proposes a convex optimization method for signal recovery on manifolds from one-bit measurements, with better error bounds and broader measurement class.

## Key findings

- Outperforms prior scalar quantization methods in error bounds
- Extends measurement models beyond Gaussian ensembles
- Validated through numerical experiments

## Abstract

We study the problem of approximately recovering signals on a manifold from one-bit linear measurements drawn from either a Gaussian ensemble, partial circulant ensemble, or bounded orthonormal ensemble and quantized using Sigma-Delta or distributed noise shaping schemes. We assume we are given a Geometric Multi-Resolution Analysis, which approximates the manifold, and we propose a convex optimization algorithm for signal recovery. We prove an upper bound on the recovery error which outperforms prior works that use memoryless scalar quantization, requires a simpler analysis, and extends the class of measurements beyond Gaussians. Finally, we illustrate our results with numerical experiments.