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Chapitre D'ouvrage Année : 2021

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

Résumé

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 fast algorithm to compute the dominant singular subspaces of a Q-order tensor. As opposedt o the state of the art methods (usually called HOSVD for high-order SVD), our approach mitigates the well-known “curse of dimentionality”. This approach is applied to speed up kernel-based supervised tensor classification.
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Dates et versions

hal-03264296 , version 1 (18-06-2021)

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  • HAL Id : hal-03264296 , version 1

Citer

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, 2021, Tensors for Data Processing. ⟨hal-03264296⟩
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