# Online Orthogonal Matching Pursuit

arXiv: https://arxiv.org/abs/2011.11117 · HTML: https://mlsanity.com/paper/2011.11117/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

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

- [CheckSel: Efficient and Accurate Data-valuation Through Online Checkpoint Selection](https://mlsanity.com/paper/2203.06814/) (2022)

## Abstract (arXiv)

Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models. In classical procedures, the main emphasis was put on the sample complexity, with little or no consideration of the computation resources required. We present a novel online algorithm: Online Orthogonal Matching Pursuit (OOMP) for online support recovery in the random design setting of sparse linear regression. Our procedure selects features sequentially, alternating between allocation of samples only as needed to candidate features, and optimization over the selected set of variables to estimate the regression coefficients. Theoretical guarantees about the output of this algorithm are proven and its computational complexity is analysed.