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Chapitre d'ouvrage

Structured Tensor-Train Decomposition for Speeding-Up Kernel-Based Learning

Abstract : In this chapter, we present an algebraic relation between the Tucker model and the Tensor-Train decomposition with structured cores. Exploiting this link, we present a new fastalgorithm to compute the dominant singular subspaces of a푄-order tensor. As opposedto the state of the art methods (usually called HOSVD for high-order SVD), our approachmitigates the well-known “curse of dimentionality”. This approach is applied to speedup kernel-based supervised tensor classification.
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Chapitre d'ouvrage
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https://hal.univ-lille.fr/hal-03264296
Contributeur : Remy Boyer <>
Soumis le : vendredi 18 juin 2021 - 09:42:16
Dernière modification le : mardi 22 juin 2021 - 18:19:03

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  • HAL Id : hal-03264296, version 1

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Yassine Zniyed, Ouafae Karmouda, Jérémie Boulanger, Remy Boyer, André L. F. de Almeida, et al.. Structured Tensor-Train Decomposition for Speeding-Up Kernel-Based Learning. Yipeng Liu. Tensors for Data Processing, Chapter 15, Elsevier, In press, Tensors for Data Processing. ⟨hal-03264296⟩

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