Riikka Huusari, Hachem Kadri
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2578-2584. https://doi.org/10.24963/ijcai.2019/358
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.
Machine Learning: Kernel Methods
Machine Learning: Multi-instance;Multi-label;Multi-view learning