Multiple Operator-valued Kernel Learning

TL;DR

This paper introduces a novel method for learning combinations of operator-valued kernels to improve multi-output functional data analysis, addressing complex optimization challenges with a new algorithm validated on brain-computer interface data.

Contribution

It proposes a multiple operator-valued kernel learning algorithm for functional data, extending kernel ridge regression to nonlinear multi-output contexts with a block coordinate descent approach.

Findings

Effective in functional regression for finger movement prediction

Addresses theoretical challenges of operator-valued kernel optimization

Validated on brain-computer interface data

Abstract

Positive definite operator-valued kernels generalize the well-known notion of reproducing kernels, and are naturally adapted to multi-output learning situations. This paper addresses the problem of learning a finite linear combination of infinite-dimensional operator-valued kernels which are suitable for extending functional data analysis methods to nonlinear contexts. We study this problem in the case of kernel ridge regression for functional responses with an lr-norm constraint on the combination coefficients. The resulting optimization problem is more involved than those of multiple scalar-valued kernel learning since operator-valued kernels pose more technical and theoretical issues. We propose a multiple operator-valued kernel learning algorithm based on solving a system of linear operator equations by using a block coordinatedescent procedure. We experimentally validate our approach on a functional regression task in the context of finger movement prediction in brain-computer interfaces.