Tensors: a partial survey

Pierre Comon (1)

1 GIPSA-lab - Grenoble Images Parole Signal Automatique

Tensor decompositions are at the core of many Blind Source Separation (BSS) algorithms, either explicitly or implicitly. In particular, the Canonical Polyadic (CP) tensor decomposition plays a central role in identification of underdetermined mixtures. Despite some similarities, CP and Singular value Decomposition (SVD) are quite different. More generally, tensors and matrices enjoy different properties, as pointed out in this brief survey.

Mots clés: tensor; blind source separation; CP decomposition; polynomial; polyadic; Waring

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Pierre Comon. Tensors: a partial survey. Signal Processing Magazine, 2014, pp.11. ⟨hal-00923279v1⟩