Abstract
We consider the problem of operator-valued kernel
learning and investigate the possibility of going beyond the well-known separable kernels. Borrowing
tools and concepts from the field of quantum computing, such as partial trace and entanglement, we
propose a new view on operator-valued kernels and
define a general family of kernels that encompasses
previously known operator-valued kernels, including separable and transformable kernels. Within
this framework, we introduce another novel class
of operator-valued kernels called entangled kernels
that are not separable. We propose an efficient twostep algorithm for this framework, where the entangled kernel is learned based on a novel extension of
kernel alignment to operator-valued kernels. The
utility of the algorithm is illustrated on both artifi-
cial and real data