Multiple Locally Linear Kernel Machines
This work addresses the problem of improving computational efficiency for non-linear classifiers in machine learning, though it appears incremental as it builds on existing Multiple Kernel Learning methods.
The paper tackles the trade-off between accuracy and speed in non-linear classification by proposing a new classifier that combines locally linear classifiers, achieving a balance between high accuracy and fast inference time.
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.