Stochastic Low-Rank Kernel Learning for Regression
This is an incremental improvement for kernel regression methods, offering automatic kernel design and efficient updates.
The authors tackled kernel-based regression by learning conical combinations of parameterized kernels using a new stochastic convex optimization procedure with convergence guarantees, achieving competitive performance on benchmark datasets.
We present a novel approach to learn a kernel-based regression function. It is based on the useof conical combinations of data-based parameterized kernels and on a new stochastic convex optimization procedure of which we establish convergence guarantees. The overall learning procedure has the nice properties that a) the learned conical combination is automatically designed to perform the regression task at hand and b) the updates implicated by the optimization procedure are quite inexpensive. In order to shed light on the appositeness of our learning strategy, we present empirical results from experiments conducted on various benchmark datasets.