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 $\ell_1$ 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.

翻译：本文提出一种基于局部线性分类器组合的新型非线性分类器。通过将问题转化为使用大量局部线性核的ℓ₁多核学习（MKL）问题，给出了成熟的优化公式。由于此类核数量庞大，我们提出了一种可扩展的通用MKL训练算法来处理流式核。在推理时间方面，该分类器填补了高精度但慢速的非线性分类器（如传统MKL）与快速但低精度的线性分类器之间的空白。