# Sparse Linear Regression via Generalized Orthogonal Least-Squares

arXiv: https://arxiv.org/abs/1602.06916 · HTML: https://mlsanity.com/paper/1602.06916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2016.

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

- [Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications](https://mlsanity.com/paper/2107.10484/) (2021)

## Abstract (arXiv)

Sparse linear regression, which entails finding a sparse solution to an underdetermined system of linear equations, can formally be expressed as an l0-constrained least-squares problem. The Orthogonal Least-Squares (OLS) algorithm sequentially selects the features (i.e., columns of the coefficient matrix) to greedily find an approximate sparse solution. In this paper, a generalization of Orthogonal Least-Squares which relies on a recursive relation between the components of the optimal solution to select L features at each step and solve the resulting overdetermined system of equations is proposed. Simulation results demonstrate that the generalized OLS algorithm is computationally efficient and achieves performance superior to that of existing greedy algorithms broadly used in the literature.