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Pré-Publication, Document De Travail Année : 2020

Tensor Methods for Multi-Aspect Trajectory Data Mining

Eleftherios Kofidis

Résumé

Tensor models have been well established as a natural and powerful way of representing systems and data that involve multiple aspects/dimensions. Assisted by their unique ability to unveil latent information through tensor decomposition methods, they have proved successful in numerous applications. It is thus of no surprise that they have also been employed for trajectory data mining applications, especially those that enrich mobility data with additional, generally heterogeneous aspects. This article aims at providing a brief overview of the related literature.
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Dates et versions

hal-02639989 , version 1 (28-05-2020)

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  • HAL Id : hal-02639989 , version 1

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Eleftherios Kofidis. Tensor Methods for Multi-Aspect Trajectory Data Mining. 2020. ⟨hal-02639989⟩
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