Accelerating likelihood optimization for ICA on real signals

Abstract : We study optimization methods for solving the maximum likelihood formulation of independent component analysis (ICA). We consider both the the problem constrained to white signals and the unconstrained problem. The Hessian of the objective function is costly to compute, which renders Newton's method impractical for large data sets. Many algorithms proposed in the literature can be rewritten as quasi-Newton methods, for which the Hessian approximation is cheap to compute. These algorithms are very fast on simulated data where the linear mixture assumption really holds. However, on real signals, we observe that their rate of convergence can be severely impaired. In this paper, we investigate the origins of this behavior, and show that the recently proposed Preconditioned ICA for Real Data (Picard) algorithm overcomes this issue on both constrained and unconstrained problems.
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https://hal.inria.fr/hal-01822602
Contributor : Pierre Ablin <>
Submitted on : Monday, June 25, 2018 - 12:47:07 PM
Last modification on : Monday, February 10, 2020 - 6:13:44 PM
Long-term archiving on: Wednesday, September 26, 2018 - 1:15:30 PM

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  • HAL Id : hal-01822602, version 1
  • ARXIV : 1806.09390

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Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort. Accelerating likelihood optimization for ICA on real signals. LVA-ICA 2018, Jul 2018, Guildford, United Kingdom. ⟨hal-01822602⟩

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