Proceedings

Entangled Kernels

Riikka Huusari, Hachem Kadri

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Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Main track. Pages 2578-2584.

https://doi.org/10.24963/ijcai.2019/358

Keywords

  • Machine Learning: Kernel Methods
  • Machine Learning: Multi-instance;Multi-label;Multi-view learning

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.

Riikka Huusari, and Hachem Kadri. 2019. Entangled Kernels. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. IJCAI, 2578–2584. https://doi.org/10.24963/ijcai.2019/358
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