Pattern recognition (psychology)

Iterative method

Mathematics

Computer science

Independent component analysis

Mathematical optimization

Broyden–Fletcher–Goldfarb–Shanno algorithm

Component (thermodynamics)

Thermodynamics

Algorithm

Preconditioner

Asynchronous communication

Artificial intelligence

Hessian matrix

Applied mathematics

Mathematical analysis

Iterated function

Physics

Computer network

IEEE Transactions on Signal Processing

Independent Component Analysis (ICA) is a technique for unsupervised exploration of multi-channel data that is widely used in observational sciences. In its classic form, ICA relies on modeling the data as linear mixtures of non-Gaussian independent sources. The maximization of the corresponding likelihood is a challenging problem if it has to be completed quickly and accurately on large sets of real data. We introduce the Preconditioned ICA for Real Data (Picard) algorithm, which is a relative L-BFGS algorithm preconditioned with sparse Hessian approximations. Extensive numerical comparisons to several algorithms of the same class demonstrate the superior performance of the proposed technique, especially on real data, for which the ICA model does not necessarily hold.

Faster Independent Component Analysis by Preconditioning With Hessian Approximations