Multiple Locally Linear Kernel Machines
David Picard
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.