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Entangled Kernels

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.
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Contributor : Riikka Huusari <>
Submitted on : Wednesday, July 17, 2019 - 4:16:07 PM
Last modification on : Tuesday, October 15, 2019 - 11:16:12 AM


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Riikka Huusari, Hachem Kadri. Entangled Kernels. International Joint Conference of Artificial Intelligence, Aug 2019, Macao, China. pp.2578-2584, ⟨10.24963/ijcai.2019/358⟩. ⟨hal-02187162⟩



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