# Reducing Basis Mismatch in Harmonic Signal Recovery via Alternating Convex Search

arXiv: https://arxiv.org/abs/1406.5231 · HTML: https://mlsanity.com/paper/1406.5231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2014.

## Built on by (most cited first, 1 of 1)

- [Compressive Recovery of Signals Defined on Perturbed Graphs](https://mlsanity.com/paper/2402.07637/) (2024)

## Abstract (arXiv)

The theory behind compressive sampling pre-supposes that a given sequence of observations may be exactly represented by a linear combination of a small number of basis vectors. In practice, however, even small deviations from an exact signal model can result in dramatic increases in estimation error; this is the so-called "basis mismatch" problem. This work provides one possible solution to this problem in the form of an iterative, biconvex search algorithm. The approach uses standard ℓ1-minimization to find the signal model coefficients followed by a maximum likelihood estimate of the signal model. The algorithm is illustrated on harmonic signals of varying sparsity and outperforms the current state-of-the-art.