# Compressed Sensing for Finite-Valued Signals

arXiv: https://arxiv.org/abs/1609.09450 · HTML: https://mlsanity.com/paper/1609.09450/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2016.

## Built on by (most cited first, 2 of 2)

- [Hardness and Algorithms for Robust and Sparse Optimization](https://mlsanity.com/paper/2206.14354/) (2022)
- [Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces](https://mlsanity.com/paper/1905.09700/) (2019)

## Abstract (arXiv)

The need of reconstructing discrete-valued sparse signals from few measurements, that is solving an undetermined system of linear equations, appears frequently in science and engineering. Whereas classical compressed sensing algorithms do not incorporate the additional knowledge of the discrete nature of the signal, classical lattice decoding approaches such as the sphere decoder do not utilize sparsity constraints. In this work, we present an approach that incorporates a discrete values prior into basis pursuit. In particular, we address unipolar binary and bipolar ternary sparse signals, i.e., sparse signals with entries in 0,1, respectively in -1,0,1. We will show that phase transition takes place earlier than when using the classical basis pursuit approach and that, independently of the sparsity of the signal, at most N/2, respectively 3N/4, measurements are necessary to recover a unipolar binary, and a bipolar ternary signal uniquely, where N is the dimension of the ambient space. We will further discuss robustness of the algorithm and generalizations to signals with entries in larger alphabets.