# Multiple Locally Linear Kernel Machines

arXiv: https://arxiv.org/abs/2401.09629 · HTML: https://mlsanity.com/paper/2401.09629/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization](https://mlsanity.com/paper/1309.2375/) (2013)

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

- [Locally Adaptive One-Class Classifier Fusion with Dynamic ℓp-Norm Constraints for Robust Anomaly Detection](https://mlsanity.com/paper/2411.06406/) (2024)

## Abstract (arXiv)

In this paper we propose a new non-linear classifier based on a combination of locally linear classifiers. A well known optimization formulation is given as we cast the problem in a ℓ1 Multiple Kernel Learning (MKL) problem using many locally linear kernels. Since the number of such kernels is huge, we provide a scalable generic MKL training algorithm handling streaming kernels. With respect to the inference time, the resulting classifier fits the gap between high accuracy but slow non-linear classifiers (such as classical MKL) and fast but low accuracy linear classifiers.