# Deterministic Bridge Regression for Compressive Classification

arXiv: https://arxiv.org/abs/2203.09721 · HTML: https://mlsanity.com/paper/2203.09721/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Penalized regression via the restricted bridge estimator](https://mlsanity.com/paper/1910.03660/) (2019)
- [A wonderful triangle in compressed sensing](https://mlsanity.com/paper/2202.09952/) (2022)

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

- [Decoupled Weight Decay for Any p Norm](https://mlsanity.com/paper/2404.10824/) (2024)

## Abstract (arXiv)

Pattern classification with compact representation is an important component in machine intelligence. In this work, an analytic bridge solution is proposed for compressive classification. The proposal has been based upon solving a penalized error formulation utilizing an approximated ℓp-norm. The solution comes in a primal form for over-determined systems and in a dual form for under-determined systems. While the primal form is suitable for problems of low dimension with large data samples, the dual form is suitable for problems of high dimension but with a small number of data samples. The solution has also been extended for problems with multiple classification outputs. Numerical studies based on simulated and real-world data validated the effectiveness of the proposed solution.