PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Canonical Tensor Decompositions
Pierre Comon
In: ARCC Workshop on Tensor Decomposition, July 18 - 24, 2004, Palo Alto, California.

Abstract

The Singular Value Decomposition (SVD) may be extended to tensors at least in two very different ways. One is the High-Order SVD (HOSVD), and the other is the Canonical Decomposition (CanD). Only the latter is closely related to the Tensor Rank. Important basic questions are raised in this short paper, such as the maximal achievable rank of a tensor of given dimensions, or the computation of a CanD. Some questions are answered, and it turns out that the answers depend on the choice of the underlying field, and on tensor symmetry structure, which outlines a major difference compared to matrices.

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EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:412
Deposited By:Pierre Comon
Deposited On:19 December 2004