Non-negative Independent Component Analysis Algorithm Based on 2D Givens Rotations and a Newton Optimization

Wendyam S. B. Ouedraogo (1, 2, 3) , Antoine Souloumiac (1) , Christian Jutten (4)

1 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies
2 Unité Signaux et Systèmes
3 GIPSA-lab - Grenoble Images Parole Signal Automatique
4 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

In this paper, we consider the Independent Component Analysis problem when the hidden sources are non-negative (Non-negative ICA). This problem is formulated as a non-linear cost function optimization over the special orthogonal matrix group SO(n). Using Givens rotations and Newton optimization, we developed an effective axis pair rotation method for Non-negative ICA. The performance of the proposed method is compared to those designed by Plumbley and simulations on synthetic data show the efficiency of the proposed algorithm.

Mots clés: ICA; NMF; Givens rotation

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

Wendyam S. B. Ouedraogo, Antoine Souloumiac, Christian Jutten. Non-negative Independent Component Analysis Algorithm Based on 2D Givens Rotations and a Newton Optimization. LVA/ICA 2010 - 9th International Conference on Latent Variable Analysis and Signal Separation, Sep 2010, Saint Malo, France. pp.522-529. ⟨hal-00526058⟩