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 two-step 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 artificial and real data.
Keywords
- Machine Learning: Kernel Methods
- Machine Learning: Multi-instance;Multi-label;Multi-view learning
