# Multidimensional unstructured sparse recovery via eigenmatrix

arXiv: https://arxiv.org/abs/2402.17215 · HTML: https://mlsanity.com/paper/2402.17215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Efficient Algorithms for Sparse Moment Problems without Separation](https://mlsanity.com/paper/2207.13008/) (2022)
- [Eigenmatrix for unstructured sparse recovery](https://mlsanity.com/paper/2311.16609/) (2023)

## Abstract (arXiv)

This note considers the multidimensional unstructured sparse recovery problems. Examples include Fourier inversion and sparse deconvolution. The eigenmatrix is a data-driven construction with desired approximate eigenvalues and eigenvectors proposed for the one-dimensional problems. This note extends the eigenmatrix approach to multidimensional problems. Numerical results are provided to demonstrate the performance of the proposed method.