Breaking the curse of dimensionality for ...
Type de document :
Communication dans un congrès avec actes
Titre :
Breaking the curse of dimensionality for coupled matrix-tensor factorization
Auteur(s) :
Boudehane, Abdelhak [Auteur]
Laboratoire des signaux et systèmes [L2S]
Zniyed, Yassine [Auteur]
Laboratoire des signaux et systèmes [L2S]
Tenenhaus, Arthur [Auteur]
Laboratoire des signaux et systèmes [L2S]
Le Brusquet, Laurent [Auteur]
Laboratoire des signaux et systèmes [L2S]
Boyer, Remy [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Laboratoire des signaux et systèmes [L2S]
Zniyed, Yassine [Auteur]
Laboratoire des signaux et systèmes [L2S]
Tenenhaus, Arthur [Auteur]
Laboratoire des signaux et systèmes [L2S]
Le Brusquet, Laurent [Auteur]
Laboratoire des signaux et systèmes [L2S]
Boyer, Remy [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019)
Ville :
Le Gosier
Pays :
Guadeloupe
Date de début de la manifestation scientifique :
2019-12-15
Mot(s)-clé(s) en anglais :
Coupled matrix tensor decomposition
Tensor train
Heterogeneous data analysis
Joint estimation
Fast algorithms
Tensor train
Heterogeneous data analysis
Joint estimation
Fast algorithms
Discipline(s) HAL :
Mathématiques [math]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
In different application fields, heterogeneous data sets are structured into either matrices or higher-order tensors. In some cases, these structures present the property of having common underlying factors, which is used ...
Lire la suite >In different application fields, heterogeneous data sets are structured into either matrices or higher-order tensors. In some cases, these structures present the property of having common underlying factors, which is used to improve the efficiency of factor-matrices estimation in the process of the so-called coupled matrix-tensor factorization (CMTF). Many methods target the CMTF problem relying on alternating algorithms or gradient approaches. However, computational complexity remains a challenge when the data sets are tensors of high-order, which is linked to the well-known "curse of dimensionality". In this paper, we present a methodologi-cal approach, using the Joint dImensionality Reduction And Factors rEtrieval (JIRAFE) algorithm for joint factorization of high-order tensor and matrix. This approach reduces the high-order CMTF problem into a set of 3-order CMTF and canonical polyadic decomposition (CPD) problems. The proposed algorithm is evaluated on simulation and compared with a gradient-based method.Lire moins >
Lire la suite >In different application fields, heterogeneous data sets are structured into either matrices or higher-order tensors. In some cases, these structures present the property of having common underlying factors, which is used to improve the efficiency of factor-matrices estimation in the process of the so-called coupled matrix-tensor factorization (CMTF). Many methods target the CMTF problem relying on alternating algorithms or gradient approaches. However, computational complexity remains a challenge when the data sets are tensors of high-order, which is linked to the well-known "curse of dimensionality". In this paper, we present a methodologi-cal approach, using the Joint dImensionality Reduction And Factors rEtrieval (JIRAFE) algorithm for joint factorization of high-order tensor and matrix. This approach reduces the high-order CMTF problem into a set of 3-order CMTF and canonical polyadic decomposition (CPD) problems. The proposed algorithm is evaluated on simulation and compared with a gradient-based method.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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