A recent result in the literature states that polynomial and conjunctive features can be hierarchically organized and described by different kernels of increasing expressiveness (or complexity). Additionally, the optimal combination of those kernels through a Multiple Kernel Learning approach produces effective and robust deep kernels. In this paper, we extend this approach to structured data, showing an adaptation of classical spectrum kernels, here named monotone spectrum kernels, reflecting a hierarchical feature space of sub-structures of increasing complexity. Finally, we show that (i) our kernels adaptation does not differ significantly from classical spectrum kernels, and (ii) the optimal combination achieves better results than the single spectrum kernel.

Monotone Deep Spectrum Kernels

Lauriola I.;Aiolli F.
2020

Abstract

A recent result in the literature states that polynomial and conjunctive features can be hierarchically organized and described by different kernels of increasing expressiveness (or complexity). Additionally, the optimal combination of those kernels through a Multiple Kernel Learning approach produces effective and robust deep kernels. In this paper, we extend this approach to structured data, showing an adaptation of classical spectrum kernels, here named monotone spectrum kernels, reflecting a hierarchical feature space of sub-structures of increasing complexity. Finally, we show that (i) our kernels adaptation does not differ significantly from classical spectrum kernels, and (ii) the optimal combination achieves better results than the single spectrum kernel.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
978-3-030-61608-3
978-3-030-61609-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3382905
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