International audienceWith the increase in measurement/sensing technologies, the collected data are in intrinsically multidimensional in a large number of applications. This can be interpreted as a growth of the dimensionality/order of the associated tensor. There exists therefore a crucial need to derive equivalent and alternative models of a high-order tensor as a graph of low-order tensors. In this work we consider a " train " graph, i.e., a Q-order tensor will be represented as a Tensor Train (TT) composed of Q − 2 3-order core tensors and two core matrices. In this context, it has been shown that a canonical rank-R CPD / PARAFAC model can always be represented exactly by a TT model whose cores are canonical rank-R CPD/PARAFAC. This mod...
We study uniqueness of the decomposition of an nth order tensor (also called n-way array) into a sum...
Multidimensional data, or tensors, arise natura lly in data analysis applications. Hitchcock&##39;s ...
In this thesis, we develop an algebraic and graph theoretical reinterpretation of tensor networks an...
International audienceWith the increase in measurement/sensing technologies, the collected data are ...
In this paper, we derive improved uniqueness conditions for a constrained version of the canonical o...
International audienceIn this work, equivalence relations between a Tensor Train (TT) decomposition ...
International audienceSome tensor prerequisites with a particular emphasis on mode combination using...
International audienceThe canonical polyadic decomposition (CPD) is one of the most popular tensor-b...
In many applications signals or data vary with respect to several parameters (such as spatial coord...
Copyright © by SIAM. Coupled tensor decompositions are becoming increasingly important in signal pro...
© 2018 John Wiley & Sons, Ltd. Real-life data often exhibit some structure and/or sparsity, allowi...
The tensor rank decomposition or CPD expresses a tensor as a minimum-length linear combination of el...
International audienceWe propose a new framework for tensor decomposition based on trace invariants,...
Canonical Polyadic (also known as Candecomp/Parafac) Decomposition (CPD) of a higher-order tensor is...
We study uniqueness of the decomposition of an nth order tensor (also called n-way array) into a sum...
Multidimensional data, or tensors, arise natura lly in data analysis applications. Hitchcock&##39;s ...
In this thesis, we develop an algebraic and graph theoretical reinterpretation of tensor networks an...
International audienceWith the increase in measurement/sensing technologies, the collected data are ...
In this paper, we derive improved uniqueness conditions for a constrained version of the canonical o...
International audienceIn this work, equivalence relations between a Tensor Train (TT) decomposition ...
International audienceSome tensor prerequisites with a particular emphasis on mode combination using...
International audienceThe canonical polyadic decomposition (CPD) is one of the most popular tensor-b...
In many applications signals or data vary with respect to several parameters (such as spatial coord...
Copyright © by SIAM. Coupled tensor decompositions are becoming increasingly important in signal pro...
© 2018 John Wiley & Sons, Ltd. Real-life data often exhibit some structure and/or sparsity, allowi...
The tensor rank decomposition or CPD expresses a tensor as a minimum-length linear combination of el...
International audienceWe propose a new framework for tensor decomposition based on trace invariants,...
Canonical Polyadic (also known as Candecomp/Parafac) Decomposition (CPD) of a higher-order tensor is...
We study uniqueness of the decomposition of an nth order tensor (also called n-way array) into a sum...
Multidimensional data, or tensors, arise natura lly in data analysis applications. Hitchcock&##39;s ...
In this thesis, we develop an algebraic and graph theoretical reinterpretation of tensor networks an...