Faster ICA under orthogonal constraint - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

Faster ICA under orthogonal constraint


Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data widely used in observational sciences. In its classical form, ICA relies on modeling the data as a linear mixture of non-Gaussian independent sources. The problem can be seen as a likelihood maximization problem. We introduce Picard-O, a preconditioned L-BFGS strategy over the set of orthogonal matrices, which can quickly separate both super-and sub-Gaussian signals. It returns the same set of sources as the widely used FastICA algorithm. Through numerical experiments, we show that our method is faster and more robust than FastICA on real data.
Fichier principal
Vignette du fichier
picardo-arxiv.pdf (493.38 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01651842 , version 1 (29-11-2017)


  • HAL Id : hal-01651842 , version 1


Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort. Faster ICA under orthogonal constraint. International Conference on Acoustics, Speech, & Signal Processing, 2018, Calgary, Canada. ⟨hal-01651842⟩
376 View
973 Download


Gmail Facebook X LinkedIn More