Joint Analysis of Multiple Datasets by Cross-Cumulant Tensor (Block) Diagonalization

Dana Lahat (1) , Christian Jutten (1)

1 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

In this paper, we propose approximate diagonalization of a cross-cumulant tensor as a means to achieve independent component analysis (ICA) in several linked datasets. This approach generalizes existing cumulant-based independent vector analysis (IVA). It leads to uniqueness, identifiability and resilience to noise that exceed those in the literature, in certain scenarios. The proposed method can achieve blind identification of underdetermined mixtures when single-dataset cumulant-based methods that use the same order of statistics fall short. In addition, it is possible to analyse more than two datasets in a single tensor factorization. The proposed approach readily extends to independent subspace analysis (ISA), by tensor block-diagonalization. The proposed approach can be used as-is or as an ingredient in various data fusion frameworks, using coupled decompositions. The core idea can be used to generalize existing ICA methods from one dataset to an ensemble.

Mots clés: Independent vector analysis; tensor diagonalization; blind source separation; coupled decompositions; data fusion

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

Dana Lahat, Christian Jutten. Joint Analysis of Multiple Datasets by Cross-Cumulant Tensor (Block) Diagonalization. SAM 2016 - 9th IEEE Sensor Array and Multichannel Signal Processing Workshop, Jul 2016, Rio de Janeiro, Brazil. ⟨hal-01351209⟩